1
Purification of Recombinant Vaccinia Virus for
Oncolytic and Immunotherapeutic Applications using
Monolithic Column Technology
A thesis submitted to University College London
for the degree of Doctor of Engineering
by
David Isaac William Vincent
The Advanced Centre for Biochemical Engineering
Department of Biochemical Engineering
University College London
Gordon Street
London WC1H 0AH
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I, David Vincent confirm that the work presented in this thesis is my own. Where
information has been derived from other sources, I confirm that this has been indicated
in the thesis.
Signed……………….………………………Date………………………………………
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Acknowledgements
I would like to thank my supervisor Dr Yuhong Zhou for her invaluable feedback and
support over the course of this EngD. Without her continuous encouragement this work
would likely never have been completed.
I would also like to thank my Advisor Prof. Ajoy Velayudhan for his help and guidance.
Many a long discussion was had on the intricacies of vaccinia viral purification which
were thought provoking, inspirational but also really good fun.
I would also like to express my gratitude to the staff and students at UCL. There are too
many to mention everyone, but in particular I would like to thank Mrs Ludmila Ruban,
Miss Dhushy Stanislaus and Dr Tarit Mukhopadhyay for their support, especially in
setting up the vaccine lab in the department which I could benefit from in my final year.
I would also like to thank Dr Patricia Perez Esteban for her support, friendship and
bioprocess wisdom.
I would like to express my gratitude to BIA Separations and the EPSRC for sponsoring
this work. Without this financial support this work would not have been possible. At
BIA Separations I would especially like to thank Dr Aleš Štrancer, Dr Petra
Kramberger, and Dr Rosana Hudej for their guidance and technical expertise.
At lot of this work was unexpectedly performed at Barts Cancer Institute, Queen Mary
University of London. I would like to express my gratitude to the institute for being so
accommodating, and especially like to thank Prof Yaohe Wang, Dr Ming Yuan, Dr
Rathi Gangeswaren, Dr Chadwan Al-Yaghchi and Dr Jahangir Ahmed for their
encouragement, assistance, and friendship.
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Finally I would like to thank my family for sticking by me during the last 4+ years. I
want to particularly thank my amazing wife Ruxandra for her love, support and
guidance in all aspects of my life and work. During this work, my beautiful son was
born, Joseph John Maurice Comisel-Vincent. Welcome to the world, I hope you find
Daddy’s work interesting and useful.
Also Angela you owe me £5.
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Abstract
Vaccinia virus Lister strain, a large, structurally complex double stranded DNA pox
virus, is being developed by a number of organisations around the world as an oncolytic
and immunotherapeutic agent for the treatment of a broad range of cancers. Should
these therapies prove to be efficacious in the clinic, large quantities of vaccinia virus
will need to be produced at very high levels of purity as dosing requirements are
expected to be as high as approximately 1x109 pfu/dose.
In this thesis, the development of two convective interaction media (CIM) monolith
capture steps for vaccinia virus with considerable purification factors is described; one
uses cation exchange while the other uses hydrophobic interaction chromatography. The
purification process development involved an extensive material characterisation study
resulting in enhanced product understanding, a rapid resin screening study aimed at
quickly identifying suitable resin chemistries, followed by process optimisation studies
on the best performing monoliths.
After being challenged with crude infectious vaccinia harvest, CIM OH monoliths are
shown to be able to recover up to 90% of the infectious virus loaded whilst removing up
to 99% of the contaminating DNA (without nuclease treatment) and 100% of
quantifiable protein. Binding capacities were shown to be in the order of 1x109 pfu/mL.
The high levels of both batch to batch and assay variability as well as the tendency of
vaccinia virus to aggregate in the feed material, typical of viral processes especially
when developed alongside un-optimised upstream conditions, are clearly demonstrated
and the implications are explored. The results show that it can be challenging to draw
robust conclusions on process performance. To minimise the effects of analytical
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variability, a number of orthogonal analytical methods have been used to quantify and
characterise viral particles. These include TCID50, nanoparticle tracking analysis
(NTA), tunable resistive pulse sensing (TRPS), transmission electron microscopy
(TEM), scanning electron microscopy (SEM), and real-time PCR (qPCR). In order to
put this into an industrial context, a comparative cost of goods analysis of monoliths and
ultracentrifugation technologies for the purification of large viral particles is provided.
This shows that both chromatography using monoliths and continuous flow
ultracentrifugation can be economically viable, although both have limitations. The
potential economic benefits of using a monolith-based process over an
ultracentrifugation-based process are increased productivity, the ability to generate
purer material and ease of scale-up.
CIM monoliths are unique stationary phases that offer efficient separation and high
productivity owing to fast cycle times and high binding capacities. Both cation
exchange (CIM SO3) and hydrophobic interaction (CIM OH) monoliths are effectual at
removing the majority of contaminants in a single purification step that can easily be
scaled up to 8 L bed volumes. CIM monoliths have the potential to be an attractive
option for future manufacturing processes for oncolytic viral therapies. They are shown
in this thesis to achieve a higher percentage recovery and better removal of DNA,
protein and aggregate than any other technology described in the literature to date.
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Impact Statement
The gene and immunotherapy field has picked up a lot in the last few years after some
initial setbacks in the clinic. A number of in vivo and ex vivo viral constructs have
recently entered clinical trials (Heo et al. 2013).
Vaccinia virus has shown promising efficacy in both animal models and late stage
cancer patients for both solid tumours and models for metastasis (Garber 2006;Kirn and
Thorne 2009;Tysome et al. 2009;Tysome et al. 2011). While challenges associated with
oncolytic viral therapies do exist (Ferguson et al. 2012), there is a chance that vaccinia
virus will need to be produced for direct injection into patients at a commercial scale.
The scale of this production will of course depend on the efficacy of the trial, the
indication, and the dose required. Clinical trials currently suggest that the total
treatment dose requirement will need to be relatively high in order to achieve efficacy.
It has been suggested that multiple treatments may be required each consisting of 1x109
pfu per patient (Heo, Reid, Ruo, Breitbach, Rose, Bloomston, Cho, Lim, Chung, Kim,
Burke, Lencioni, Hickman, Moon, Lee, Kim, Daneshmand, Dubois, Longpre, Ngo,
Rooney, Bell, Rhee, Patt, Hwang, & Kirn 2013). This will not only increase the costs of
production but may also result in the requirement for purer material as increasing the
amount of viral particles per dose will also increase the amount of impurities per dose,
which even at lower doses is already recognised as an issue (Wolff and Reichl 2011). It
is clear that without effective, engineering led, process development and optimisation,
production costs can become crippling, especially as vaccinia processes to date tend to
be low yielding with cell productivity less than 100 pfu/cell (Liu et al. 2016).
The impact of this work inside academia will likely be twofold. Firstly, it will advance
the levels of process knowledge and understanding of how vaccinia virus Lister strain
interacts with charged and hydrophobic monolithic stationary phases. It will also enable
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laboratories currently working with vaccinia virus Lister strain to produce material of a
higher quality, in terms of purity, than previously possible in a single step. This will be
especially relevant for groups currently relying on sucrose gradient ultracentrifugation
or similar techniques. The wider medical community will indirectly benefit from this as
the material supplied for pre-clinical and proof of concept studies will be more
representative of that produced in late stage clinical and commercial processes.
From an industrial perspective, the impact of this work, at least in the short term, will be
the addition of another purification tool in the vaccinia virus purification development
toolbox. The reason why this is important is that vaccinia is a challenging virus to
purify due to its large size and complexity, and therefore the number of purification
options available is limited. The default unit operation used to purify vaccinia and other
viruses is still ultracentrifugation. This work will add to the growing debate on whether
this technology is still of value. It adds insight into the associated cost of goods for both
technologies, suggests potential mechanisms involved in the separation of vaccinia
using CIM monoliths, and presents encouraging results indicating the production of
highly pure vaccinia virus material using chromatography.
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Table of Contents
Acknowledgements ................................................................................................................... 3
Abstract ..................................................................................................................................... 5
Impact Statement ..................................................................................................................... 7
................................................................................................................................ 28 Chapter 1
Aims and Objectives of Study ............................................................................................... 28
................................................................................................................................ 33 Chapter 2
Introduction ............................................................................................................................ 33
2.1 Vaccinia Virus .................................................................................................. 33
2.1.1 Taxonomy, Classification, and Properties of Vaccinia Virus ................... 33
2.1.2 Vaccinia Virus Life Cycle ......................................................................... 34
2.1.3 Use of Vaccinia as a Smallpox Vaccine (1967-1980) .............................. 35
2.1.4 Use of vaccinia as an oncolytic and immunotherapy vector ..................... 38
2.2 Options and Considerations When Developing Viral Bioprocesses ................ 43
2.2.1 Introduction ............................................................................................... 43
2.2.2 Cell Culture and Primary Recovery .......................................................... 44
2.2.3 Purification ................................................................................................ 48
2.3 Chromatography ............................................................................................... 57
2.3.1 Overview ................................................................................................... 57
2.3.2 Practical considerations when developing viral chromatography steps .... 60
2.3.3 Use of chromatography for virus and other macromolecular separations 64
2.4 Monoliths .......................................................................................................... 67
2.4.1 History and Overview ............................................................................... 67
2.4.2 Theoretical Considerations when using Monoliths ................................... 68
2.4.3 Purification of large complex macromolecular entities using CIM
monoliths ................................................................................................................. 75
2.5 Virus Bioprocess Analytics .............................................................................. 84
2.5.1 High Level Analytical challenges faced during virus bioprocessing ........ 84
2.5.2 Current analytical methods used in virus bioprocessing ........................... 87
2.5.3 Quantification of viral infectivity.............................................................. 93
2.6 Summary .......................................................................................................... 95
................................................................................................................................ 97 Chapter 3
Materials and Methods .......................................................................................................... 97
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3.1 Introduction ...................................................................................................... 97
3.2 Preparation of vaccinia virus load material ...................................................... 97
3.2.1 Viral production ........................................................................................ 97
3.2.2 Primary recovery ....................................................................................... 98
3.2.3 Preparation of chromatography load material ........................................... 99
3.2.4 Chromatographic systems used ............................................................... 101
3.2.5 Monolith chemistries used ...................................................................... 104
3.3 Buffer preparation .......................................................................................... 105
3.4 Analytics ......................................................................................................... 106
3.4.1 Quantification of infectious titre ............................................................. 106
3.4.2 Quantification of total viral genomes by qPCR ...................................... 108
3.4.3 Quantification of total protein ................................................................. 109
3.4.4 Visualisation of total protein by SDS-PAGE .......................................... 110
3.4.5 Quantification of total dsDNA ................................................................ 111
3.4.6 Calculation of total protein and total DNA purification factor ............... 112
3.4.7 Quantification of total particles ............................................................... 113
3.4.8 Visualisation of virus and contaminants using electron microscopy ...... 114
3.4.9 Statistical analysis ................................................................................... 115
3.5 Sample handling ............................................................................................. 117
.............................................................................................................................. 118 Chapter 4
Material Characterisation ................................................................................................... 118
4.1 Introduction .................................................................................................... 118
4.2 Harvest titre and residual impurity variability................................................ 119
4.3 Determination of vaccinia harvest Zeta Potential .......................................... 122
4.4 Virus stability ................................................................................................. 123
4.5 Physical characterisation ................................................................................ 128
4.5.1 Size distribution using nanoparticle tracking analysis (NTA) ................ 128
4.5.2 Visualisation of intact particles and evaluation of visible impurities ..... 131
4.6 Conclusions .................................................................................................... 139
.............................................................................................................................. 140 Chapter 5
Ion Exchange Chromatography Process Development .................................................... 140
5.1 Introduction .................................................................................................... 140
5.2 IEX Monolith Screening Study ...................................................................... 141
5.3 Evaluation of DNA and Protein Elution Profiles from CIM SO3 and CIM QA
Monoliths ................................................................................................................... 148
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5.4 Development of a stepwise elution schedule for CIM SO3 ........................... 151
5.5 Evaluation of CIM SO3 as a suitable capture step for vaccinia virus ............ 160
5.5.1 Evaluation into the suitability of Benzonase® as a pre-chromatographic
treatment step. ........................................................................................................ 161
5.5.2 Evaluation of CIM SO3 robustness ........................................................ 163
5.6 An illustrative description of vaccinia virus adsorption on CIM SO3 monoliths
167
5.6.1 Hypothesis 1 ............................................................................................ 167
5.6.2 Hypothesis 2 ............................................................................................ 168
5.7 Conclusion ...................................................................................................... 171
.............................................................................................................................. 173 Chapter 6
Hydrophobic Interaction Chromatography Process Development ................................. 173
6.1 Introduction .................................................................................................... 173
6.2 CIM OH monolith screening study ................................................................ 174
6.3 OH load buffer optimisation ........................................................................... 181
6.4 Evaluation of dynamic binding capacity ........................................................ 188
6.5 CIM OH step wise elution design .................................................................. 189
6.6 Evaluation of robustness and process variability ........................................... 198
6.7 Conclusions .................................................................................................... 204
.............................................................................................................................. 206 Chapter 7
An Economic Evaluation of Monolith-based processes and Comparison to Parallel
Technologies ......................................................................................................................... 206
7.1 Introduction .................................................................................................... 206
7.2 Model Design and Setup ................................................................................ 207
7.2.1 Cost breakdown ....................................................................................... 207
7.2.2 Key equations and model rules ............................................................... 208
7.3 Model Implementation ................................................................................... 211
7.3.1 Key Assumptions .................................................................................... 211
7.3.2 Process Flow and Facility Design ........................................................... 216
7.4 Comparison between Chromatography using CIM Monoliths and
Ultracentrifugation .................................................................................................... 221
7.4.1 CIM Monolith and Ultracentrifugation Base Case Process Costs
Breakdown ............................................................................................................. 221
7.4.2 CIM Monolith and Ultracentrifugation Process Sensitivity Analysis..... 224
7.5 An Economic Comparison between Chromatography using CIM Monoliths
and Ultracentrifugation. ............................................................................................. 236
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7.6 Conclusions .................................................................................................... 239
.............................................................................................................................. 240 Chapter 8
Challenges associated with the development and manufacture of infectious
vaccinia virus ........................................................................................................................ 240
8.1 Introduction .................................................................................................... 240
8.2 Process development ...................................................................................... 245
8.3 Product release ............................................................................................... 248
8.4 Consumables .................................................................................................. 248
.............................................................................................................................. 250 Chapter 9
Conclusions and Future Work ............................................................................................ 250
9.1 Conclusions .................................................................................................... 250
9.2 Future work .................................................................................................... 256
9.2.1 Pre-filtration Yield Optimisation ............................................................ 256
9.2.2 Development of an additional purification step to remove dsDNA ........ 260
9.2.3 Qualification of assays to measure residual impurities ........................... 261
............................................................................................................................ 264 Chapter 10
Appendix A ........................................................................................................................... 264
10.1 Residual Assay standard curves ..................................................................... 264
10.2 CoGs Model Parameters ................................................................................. 266
10.3 Publications by the Author ............................................................................. 268
References ............................................................................................................................. 269
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Table of Figures
Figure 2.1: Illustration to show hurdles encountered by the systemic delivery of
oncolytic viruses (Adapted from (Ferguson, Lemoine, & Wang 2012)). This shows
that the action of blood vessels, neutralising antibodies, and uptake by organs can
reduce the amount of virus available for uptake by tumour cells. ................................... 41
Figure 2.2 A graphical representation taken from (Sandra Merino 2015) to show how
large scale ALFA WASSERMAN KII ultracentrifuges work. ........................................ 50
Figure 2.3 Picture of a KII Ultracentrifuge from Alfa Wassermann (Sandra Merino
2015). ............................................................................................................................... 52
Figure 2.4 Visual comparison between packed bed, membrane adsorbers and monolith
chromatography, adapted from (Ales Strancar 2011). ..................................................... 65
Figure 2.5 An illustration to show the cross linking wide channel structure, and almost
flat velocity profile achievable using monoliths. The red arrows represent
directional flow and their size represents relative velocity. This picture was
adapted from (Gagnon 2008). .......................................................................................... 71
Figure 2.6 An illustration showing the resistances to mass transfer involved in the
adsorption of a solute molecule to a ligand within a micropore of a resin bead in a
packed bed system adapted from (Bak et al. 2007). ........................................................ 73
Figure 3.1 Process flow diagram showing method used to produce vaccinia virus
throughout this work. ..................................................................................................... 100
Figure 3.2 Chromatography system setup used for CIM SO3 stepwise elution
optimisation runs. ........................................................................................................... 101
Figure 3.3 Chromatography run setup used for CIM monolith stepwise elution runs ........... 103
Figure 3.4 qPCR standard curve generated from using chromatography load material ........ 109
Figure 4.1 Variability in infectious titre, DNA, and Protein concentration in vaccinia
virus harvest material post freeze thaw and centrifugation measured across 11
batches. ........................................................................................................................... 119
Figure 4.2 Infectious titre, DNA, and total protein concentration range in the harvest
material. (Vaccinia titre was measured across n=11 batches, DNA concentration
measured across n=6 batches and protein concentration measured across n=3
batches. ........................................................................................................................... 120
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Figure 4.3 Scatterplot matrix showing correlations between variations in concentration
of virus, DNA and protein in vaccinia harvest material. ............................................... 121
Figure 4.4 Zeta potentials of vaccinia harvest measured using the QNano showing
calculated zeta potential values for individual particles. Average value shown to be
-32 mV. .......................................................................................................................... 123
Figure 4.5 A) Vaccinia virus harvest stability time trail at room temperature. B)
Vaccinia stability study with increasing NaCl concentration in phosphate buffer pH
7 at room temperature. Points refer to raw TCID50 replicate values (n=3), mean
values shown as a horizontal line for each condition. .................................................... 125
Figure 4.6 3D histogram showing raw data values from vaccinia virus stability factorial
design. Virus was purified using 2 consecutive sucrose density gradient
ultracentrifugation runs prior to analysis. Study was performed at room
temperature over a 2 hour period in 25mM Sodium phosphate buffer. Replicate
values (n=3) have been jittered by shifting the pH by 0.1 units. A control was run
in DMEM media containing 5% FBS at the beginning, T=0, and after 2 hours T=1,
the concentration of the control samples were 8.16x109 and 1.41x1010,
respectively. ................................................................................................................... 127
Figure 4.7 Average size distribution data (n=5, 1min tracks, detection threshold 3).
Load is vaccinia virus harvest material post dilution post filtration. Null prep is
harvest material post dilution post filtration without virus, up and down stream
steps treated in the same way throughout. ..................................................................... 129
Figure 4.8 Effects of diluent salt concentration on particle size distribution. (n=5, 1min
tracks, detection threshold 5) Harvest material post dilution post filtration was
diluted 1:30 times in 50mM NaH2PO4 pH7 and 50mM NaH2PO4 1M
(NH4)2SO4 2M NaCl pH7 respectively. ...................................................................... 131
Figure 4.9 Vaccinia virus harvest material in DMEM 5% FBS 15000x magnification,
scale bar reads 1µm ........................................................................................................ 134
Figure 4.10 Vaccinia harvest diluted in CIEX load buffer 8000x magnification, scale
bar reads 2µm. ................................................................................................................ 135
Figure 4.11 TEM micrograph of Vaccinia harvest in HIC load buffer 15000x
magnification, scale bar reads 1µm ................................................................................ 136
Figure 4.12 SEM micrograph of vaccinia virus material diluted in HIC load buffer
10000x magnification, scale bar reads 1µm Image shows an extremely large
aggregated molecules potentially made up of many associated vaccinia virus
particles. ......................................................................................................................... 137
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Figure 4.13 SEM micrograph of vaccinia virus material diluted in HIC load buffer,
25000x magnification, scale bar reads 1µm................................................................... 138
Figure 5.1 Representative chromatograms from CIM S03 in Sodium Phosphate pH 7.0
(A), CIM QA in Tris pH 8.5 (B), CIM QA in HEPES pH 7.0 (C), and CIM DEAE
in Tris pH 8.5 (D). .......................................................................................................... 143
Figure 5.2 A) Box and whiskers plot (median & range) showing infectious vaccinia
recovery data from CIM SO3, CIM QA, and CIM DEAE at pH 7 and pH 8.5. Red
plots show the percentage of infections virus in the flow through fraction relative
to the load, blue plots shows percentage of infectious virus in the elution fraction
relative to the load, and black plots show the percentage infectious virus in the
elution minus the total number of particles lost in the flow through relative to the
load. B) Box and whiskers plot (median & range) showing the number of viral
particles loaded vs the capacity achieved on CIM SO3, CIM QA, and CIM DEAE. ... 144
Figure 5.3 Data shows the infectious viral particles (pfu) mass of protein (µg) and mass
of DNA (ng) in elution fractions A) Linear gradient elution run on CIM SO3
monolith loaded to approximately 3x109 pfu/mL run over 20 CV. B) Linear
gradient elution run on CIM SO3 monolith loaded to approximately 3x108 pfu/mL
and run over 25 CV. Conditions for CIM SO3 Buffer A: 50mM sodium phosphate
pH7, Buffer B: 50mM sodium phosphate 1.5M NaCl pH 7. C) Linear gradient
elution run on CIM QA monolith loaded to approx. 3x109 pfu/mL monolith run
over 20CV between 0 and 1.5M NaCl. Condition for CIM QA Loading Buffer:
50mM Tris pH 8.5. Elution Buffer: 50mM Tris 1.5M NaCl pH 8.5 ............................. 149
Figure 5.4 Data generated from CIM SO3 monoliths. A) 3D histogram illustrating the
effects of NaCl concentration and pH on DNA purification factors measured from
each run across a DoE central composite design. Raw data rather than modelled
points are shown. B) 3D histogram showing the effects of NaCl concentration and
pH on infectious viral recovery from each run across a DoE central composite
design. Face centred and internal axial points are also shown as raw data rather
than modelled points. C) DoE prediction profiler showing the modelled effects of
NaCl concentration and pH on the removal of DNA from the load. (NaCl
concentration leverage, P = 0.0007, pH leverage, P = 0.0451, AjdR2 = 0.98) (n =
2). All TCID50 measurements were performed in triplicate, DNA and protein
values measured in dublicate, mean values shown. D) Two dynamic binding
capacity (DBC) runs on CIM SO3 with ~1000 ng and ~3000 ng of DNA/mL in the
load. ................................................................................................................................ 153
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Figure 5.5 TEM micrographs of A) CIM SO3 load annotated to show contaminating
viral aggregate and debris B) SO3 eluate annotated to show vaccinia monomer.
Virus was eluted in 50 mM sodium phosphate 300mM NaCl pH 7. ............................. 158
Figure 5.6 NTA data showing size distribution of SO3 load, flow through, elution and
strip fractions. CIM SO3 monolith had been used 3 times; NTA data is showing
mean values from 5 tracking experiments, Data from run 1 (Table 5.4) ....................... 166
Figure 5.7 Schematic to show proposed binding affinity of vaccinia virus and Vaccinia-
DNA complexes on CIM S03 Monoliths. ...................................................................... 170
Figure 6.1 A) Histogram showing vaccinia virus loaded in 1 and 2 M ammonium sulfate
at pH 7 and 2M ammonium sulfate at pH 8.5. Red bars show the percentage of
infections virus in the flow through fraction relative to the load, blue bars shows
percentage of infectious virus in the elution fraction relative to the load, and black
bars show the percentage infectious virus in the elution minus the total number of
particles lost in the flow through. B) Histogram showing the number of viral
particles loaded vs the capacity achieved on CIM OH. ................................................. 177
Figure 6.2 A) Chromatogram for 1 M AS pH 7 run B) Chromatogram for 2 M AS pH 7 .... 179
Figure 6.3 A) CIM OH load optimisation study showing the percentage recovery of
infectious virus across the 0.8µm filtration step relative to the harvest, and the
recovery of infectious virus in the flow though and elution fractions relative to the
load. B) Purification factors achieved for DNA and total protein. Elution buffers
used for all experiments were 50mM sodium phosphate pH 7. ~2x109 pfu of
vaccinia was filtered, and all virus in the filtrate was loaded onto the columns. (AS
= Ammonium sulfate). ................................................................................................... 183
Figure 6.4 Replicate data showing filterability expressed as percentage recovery of virus
across the filter in different load buffers. (High g centrifugation was attempted to
remove cell debris at an RCF of ~771 for 5 min; normal centrifugation conditions
consisted of an RCF of ~440 for 5 min) (AS = Ammonium sulfate) ............................ 187
Figure 6.5 CIM OH DBC run in optimised HIC load buffer at pH 7 and pH 8. Error bars
show error in TCID50 data and are generated from 1 DBC study at each pH
measured in triplicate, error bars show 1 SD. Y-error bars are shown as shaded
areas and X-error bars are shown as horizontal lines. .................................................... 189
Figure 6.6 CIM OH Linear and stepwise elution schedules A) Linear gradient elution
over 25 CVs B) multi-stepwise elution schedule run at pH 7 C) multi-stepwise
elution schedule run at pH 8 (DNA and particle data only collected across elution
17
peak 3 rather then 5 data points plotted) D) multi-stepwise elution schedule run
with additional column volumes for 3rd and 4th steps pH 7 ......................................... 193
Figure 6.7 Non reduced SDS page gel after silver staining. Fractions are from CIM OH
harvest, load, flow though, wash, and elution fractions run at pH 7 (Figure 6.6.B)
and pH 8 (Figure 6.6.C). Null Prep fraction was prepared in the same way as
chromatography load material but did not contain virus material. FT = flow
through E=elution. ........................................................................................................ 195
Figure 6.8 Size distribution of CIM OH stepwise elution schedule load and elution
fractions. A) data includes elution fractions 3, 4 and 5 pH 7 B) data show load and
elution pH 8. N=5 for all samples, curves represent mean values. ................................ 197
Figure 6.9 A) large virus aggregate in CIMOH load (post filtration) >6µm in diameter
B) Potential Viral-DNA complex in CIM OH Load (post filtration) C) DNA type
structure with associated protein in CIM OH flow though fraction D) smaller virus
aggregate (approx. 1µm) with less DNA association in CIM OH. ................................ 200
Figure 6.10 An illustration to show proposed feed stream complexity and purification
output based on data from CIM OH. NTA is assumed to be able to count any
particle of close to 300nm (red and green rectangle (viral particles), and circles
(cell components)), qPCR will count all intact virus particles regardless of whether
infectious or not, TCID50 will only be able to count infectious virus particles, it is
unknown whether infectious particles within an aggregate is able to infect a cell.
TEM show an extremely small snap shot of the system. ............................................... 203
Figure 7.1 A) Proposed ultracentrifugation process flow diagram B) Proposed CIM
monolith process flow diagram ...................................................................................... 218
Figure 7.2 A) Facility fit out with a single ultracentrifuge B) Facility fit out with four
ultracentrifuges running in parallel. ............................................................................... 220
Figure 7.3 Costs breakdown of base case chromatographic and ultracentrifugation-based
processes ........................................................................................................................ 222
Figure 7.4 Tornado diagrams showing the effects of changes in the base case variables
shown in Table 7.5 on the overall costs per dose. A) Shows factors affecting CoGs
for ultracentrifugation processes. B) Show factors affecting CoGs for monolith
processes. All factors were changed independently with all other conditions
remaining the same as in the base case. Blue bars represent a reduction in costs per
dose while red bars represent an increase. ..................................................................... 226
Figure 7.5 Effects of Ultracentrifugation cycling and process time on the CoGs per
dose. Time periods represent the first and second ultracentrifuge step process
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times. Cycling Possible = batches are cycled on one machine. No Cycling Possible
= batches are split and run on multiple machines if required. Base case is assuming
a 20 L vol. throughput for 12hr in the first system and 1hr in the second and cannot
be cycled (Blue line in bold type script). ....................................................................... 230
Figure 7.6 Production Gantt chart to show time requirement for each unit operation in
days. This assumes each ultracentrifuge requires 50 h to band vaccinia. ...................... 232
Figure 7.7 Effects of percentage virus recovery from the first of the two
chromatography steps analysed as total cost per dose and costs per batch .................... 233
Figure 7.8 Effects of DBC and reusability of CIM Monoliths on CoG per dose. ................. 235
Figure 7.9 Overlay in overall CoG per dose to show how trade-offs in critical process
parameters can shift the balance from chromatography to ultracentrifugation.
Monolith percentage recovery is varied as trade-offs exist between virus recovery
and purity. In continuous flow ultracentrifugation the trade-offs are likely to be
between purity and volumetric throughput. ................................................................... 238
Figure 9.1 A) Proposed future study involving switching the order of the dilution and
pre-filtration steps B) Proposed order of monolith steps assuming CIM OH pre-
filtration continues to generate a low yield of infectious virus. ..................................... 257
Figure 10.1 Typical λ DNA standard curve ........................................................................... 264
Figure 10.2 Typical BSA standard curve ............................................................................... 265
19
Table of Tables
Table 2.1 List of currently available technologies for expanding adherent cell lines with
pros, cons as well as an example of commercially available systems (table not
exhaustive) ....................................................................................................................... 45
Table 2.2 Advantages and disadvantages of ultracentrifugation ............................................. 53
Table 2.3 Alternatives unit operations to ultracentrifugation .................................................. 55
Table 2.4 Commonly used analytical methods for the quantification of viral CQAs .............. 90
Table 5.1 DNA and total protein purification factors recorded during initial screening
studies on CIM SO3, CIM QA, and CIM DEAE. Buffer systems used are as
shown, a concentration of 50 mM was used in each case. Equilibration buffer did
not contain NaCl. Elution buffer contained 1.5 M NaCl. Purification factors
calculated according to Equation 3-7.. ........................................................................... 145
Table 5.2 Overview of the data generated from CIM SO3 DoE showing effects of
decreasing NaCl concentration on recovery and DNA removal and therefore
purification factor. Data in bold represents the conformation run described above
designed to maximise the DNA purification factor. *represents averaged data
points. ............................................................................................................................. 156
Table 5.3 Benzonase study showing experimental conditions including incubation time
and temperature, DNA purification factor (PF) % DNA removal in the elution
fraction, measured DNA per dose, % recovery of infectious virus, and number
column reuses are shown. Concentration of Benzonase used in all experiments was
150 U/mL. Harvest material was all from same batch and thawed at 37 ⁰C. No
addition Mg2+ Ions were added to harvest material prior to treatment with
Benzonase. DMEM contains 0.81mM Magnesium Sulfate (MgS04). RT = Room
Temperature. Control = Run performed without Benzonase® ...................................... 162
Table 5.4 experimental CIP buffers used in an attempt to regenerate a CIM SO3
monoliths. Number of particles loaded, elution buffer conditions and batch of
material used for each run was constant. Percentage infectious virus in the flow
through fraction was less than 1% for each run. Note elution peak was collected
from 100-100 mAu (Optical Path length 0.2 cm) CIP conditions shown for each
run were performed on the run before, as so where the last CIP conditions seen by
the column prior to loading. means followed by. ..................................................... 164
20
Table 6.1 Equilibration buffer and diluted viral load material conductivity and pH
conditions. Conductivity meter was uncalibrated, so it was used as a reference
only. The Chromatography system was not equipped with a pH meter. (N/T- not
tested). AS stands for ammonium sulfate. ..................................................................... 175
Table 6.2 Summary of CIM OH screening study. The number of virus particles in the
harvest and post 0.8µm filtration is shown along with percentage recovery of virus
across the filter and the column. Capacity achieved is calculated by number of
particles in the load minus the number of particles in the flow through. (* =
Indicates that a new batch of material with a lower tire was used for 2 M AS run at
pH 8.5). N/T = not tested. .............................................................................................. 180
Table 6.3 CIM OH load buffer optimisation study showing different NaCl and
(NH4)2SO4 concentrations tested. Run 1 (bold) was taken from screening
experiments. ................................................................................................................... 182
Table 6.4 CIM OH buffer optimisation study showing the number of infectious virus
particles present before and after dilution and 0.8 µm filtration, in the flow through
and elution fractions. The percentage recovery of infections virus in the flow
through and elution fractions, and the DNA and protein purification factors in the
elution fractions for each condition tested. Rows in bold represent original data
from screening study (different batch of virus, higher load volume (10mL). Buffer
optimisation study came from a single batch of virus, 2.3 mL virus was diluted and
loaded onto filter-column system. .................................................................................. 184
Table 6.5 CIM OH Multi-Stepwise elution schedule design showing percentage buffer
A and percentage buffer B for each step (Each step was 5 CVs) Row in bold (step
4) shows buffer molarity in which the majority of infectious virus was eluted. ............ 190
Table 6.6 CIM OH multi-stepwise elution scheduals run at pH 7 and pH 8 (N/T = not
tested) LLOQ = lower limit of quantification. % R = percentage recovery .................. 194
Table 7.1 Breakdown of costs and their associated components ........................................... 208
Table 7.2 Ultracentrifugation and CIM Monolithic base case assumptions .......................... 211
Table 7.3 Lists of key process costs. The “General process” costs category comprises
costs specific (referring) to unit operations that are the same regardless of the
purification strategy. This includes upstream, primary recovery and final
formulation costs. * Monolith costs are confidential so have been approximated. ....... 213
Table 7.4 Personnel remuneration costs per year (BioSolve Process Enterprise) ................. 214
Table 7.5 Factors changed in the Ultracentrifugation and Monolith sensitivity analysis.
Best case, base case and worst case scenario values are shown. ................................... 225
21
Table 7.6 Advantages and disadvantages of ultracentrifugation and chromatography and
filtration as purification unit operations for viral vaccines. Yield has been coloured
red as the recovery of infectious virus from each unit operation is dependent on the
type of virus. .................................................................................................................. 237
Table 9.1 Commercially available pre-column filters potentially applicable for vaccinia
virus prior to CIM .......................................................................................................... 259
22
Abbreviations
RSD Relative Standard Deviation
3D Three Dimensional
Ad3 Adenovirus 3
AIEX Anion Exchange Chromatography
BCA Bicinchoninic Acid Assay
BCI Bart Cancer Institute
BV Baculovirus
CEV Cell Associated Enveloped Virus
CIEX Cation Exchange Chromatography
CIM Convective Interaction Media
CMV Cytomegalovirus
CO2 Carbon Dioxide
CoGs Cost of Goods
CPE Cytopathic Effect
CV Column Volume
DBC Dynamic Binding Capacity
DEAE Diethylaminoethanol
DLS Dynamic Light Scattering
DMEM Dulbecco’s Modified Eagle’s Medium
DNA Deoxyribonucleic Acid
DoE Design of Experiment
DSP Downstream Processing
EDTA Ethylenediaminetetraacetic Acid
EEV Extracellular Enveloped Virus
EGFR Epidermal Growth Factor Receptor
ELISA Enzyme–Linked Immunosorbent Assay
EngD Engineering Doctorate
23
FDA Food and Drug Administration
fg Femtogram
GE General Electric
GFP Green Fluorescence Protein
GM-CSF Granulocyte-Macrophage Colony –Stimulating Factor
HETP Height Equivalent to a Theoretical Plate
H5 Vaccinia Virus Promoter
HCP Host Cell Protein
HETP Height Equivalence to a Theoretical Plate
HIC Hydrophobic Interaction Chromatography
HNSCC Head and Neck Squamous Cell Carcinoma
HPLC High Performance Liquid Chromatography
HRP Horse Radish Peroxidase
ID Internal Diameter
IEV Intracellular Enveloped Virus
IFN Interferon
IMV Intracellular Mature Virus
kDa Kilodalton
LLOQ Lower Limit of Quantification
LMH Litres per Meters Squared per Hour
M Molar
MA Membrane Adsorbers
mAb Monoclonal Antibody
mg Milligram
mM Millimolar
MOA Mechanism of Action
MOI Multiplicity of Infection
MVA Modified Vaccinia Ankara
NaCl Sodium Chloride
NaOH Sodium Hydroxide
24
(NH4) 2SO4 Ammonium Sulfate
NTA Nanoparticle Tracking Analysis
NYCBOH New York City Board of Health
NYVAC Highly Attenuated Strain of Vaccinia Virus
pDNA Plasmid Deoxyribonucleic Acid
pH Power of Hydrogen
PVY Potato Virus Y
QA Quality Assurance
QC Quality Control
QMUL Queen Mary University of London
qPCR Real-Time Polymerase Chain Reaction
R&D Research and Development
RMSE Root-Mean-Square-Error
RNA Ribonucleic Acid
RPLC Reverse Phase Liquid Chromatography
RT-PCR Reverse Transcription Polymerase Chain Reaction
SC Sulfated Cellulose
SDS-PAGE Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis
SEC Size Exclusion Chromatography
SEM Scanning Electron Microscopy
TBE Tris Borate EDTA
TCID50 Tissue Culture Infectious Dose 50%
TEM Transmission Electron Microscopy
TFF Tangential Flow Filtration
ToMV Tomato Mosaic Virus
TRPS Tunable Resistive Pulse Sensing
UCL University College London
UFDF Ultra Filtration Diafiltration
UPLC Ultra Performance Liquid Chromatography
USP Upstream Processing
25
VACV Vaccinia Virus
VGF Vaccinia Growth Factor
VLP Virus Like Particle
VVL15TK-
Vaccinia Virus Lister Strain 15 Thymidine Kinase Deleted
WHO World Health Organisation
WT Wild Type
26
Table of Notations
a Area available for mass transfer (m2)
A Eddy diffusion (m)
B Longitudinal diffusion (m2
s-1
)
C Resistance to mass transfer (s)
C Concentration (kg m-3
)
CAo Concentration of component A at boundary layer (kg m-3
)
CAi Concentration of component A at interphase (kg m-3
)
D Diffusion coefficient (-)
dH Hydrodynamic diameter of a particle (m)
DL Axial dispersion Coefficient (m2
s-1
)
Dp Particle diffusivity (m2 s
-1)
J Flux (kg m2 s
-1)
k Mass transfer coefficient (m s-1
)
k' Relative retention factor
Kd Dissociation constant (m3 kg
-1)
kf Film mass transfer coefficient (m s-1
)
Na Mass transfer rate (kg s-1
)
q Concentration of solute bound to adsorbent (kg m-3
)
rp Particle radius (m)
SS Sum of Squares
T Temperature (K)
t Time (s)
u Velocity (m s-1
)
V Volume (m3)
27
εb Bed porosity (-)
εp Particle porosity (-)
µ Viscosity (Pa s)
28
Chapter 1
Aims and Objectives of Study
The overall aim of this EngD project is to evaluate, understand, and work to reduce the
current challenges faced by process scientists and engineers developing vaccinia virus
constructs for vaccines and oncolytic therapies.
One such challenge is the scalability and productivity of presently used purification unit
operations, arguably the most common being ultracentrifugation. It has been suggested
that chromatography using monoliths could present an attractive alternative to
ultracentrifugation namely owing to their high binding capacity, flow independent
binding and resolution, and scalability stated by the manufacturers of commercially
available monoliths.
The virus being used in this study is Barts Cancer Institute’s back bone attenuated
vaccinia viral construct containing a RFP reporter gene (VVL15TK-RFP). The virus is
currently being developed as an oncolytic virus against a range of cancers.
To start with, an extensive vaccinia virus stability study will be performed to determine
whether there are certain conditions, such as variations in buffer salts, NaCl
concentration or pH that might affect the stability (measured by infectivity) of the virus
at room temperature over time.
Once this is ascertained, a process will be developed by screening different monolith
chemistries for ability to bind and then elute the virus. As soon as a particular chemistry
is found to reversibly bind the virus, a process of optimisation using both gradient
elution and intelligent design of experiments (DoE) to develop stepwise elution
schedules will be used. These strategies will help to determine windows of operation for
29
both binding, washing and eluting the column in order to maximise yield and purity of
the final product.
The binding capacity of vaccinia virus onto CIM monoliths should be high. Previous
work at BIA Separations used pure tomato mosaic viral preps (ToMV) with a known
mass concentration to estimate the theoretical dynamic binding capacity of ToMV on
CIM monoliths. The total particle concentration bound to the resin was estimated using
the mass of a single viral particle (Matjaž Peterka et al. 2010). This was performed by
running a DBC on a 0.34 mL monolith disk with a high concentration of pure virus
material.
The theoretical binding capacity of vaccinia virus onto 6 µm channel monoliths has
been estimated to be ~1.15x1013
particles/mL of monolith. This is assuming the
following (Mao et al. 2017).
Surface area per gram of dry monolith (BET adsorption)= 2 m2 (6 µm
channel monoliths)
Density is assumed to be 0.43 g/mL
Vaccinia is 300x250 nm (total cross sectional area = 7.5x10-14
m2
The entire monolith surface area is available for adsorption and has been
functionalised with either -OH or -SO3 groups.
Particles adsorb in ordered monolayers across the whole surface of the
monolith
Analytical methods currently used for evaluating the total number of infectious virus
particles has been described extensively in the literature as being an area requiring
increased scrutiny and validation during process development (Darling et al. 1998).
Current gold standards such as TCID50 are time consuming, involve tedious preparation,
and have been reported to give highly variable results (Stoffel et al. 2005). For this
reason a combination of orthogonal analytical measurements including TCID50,
30
nanoparticle tracking analysis (NTA), tunable resistive pulse sensing (TRPS),
transmission electron microscopy (TEM), scanning electron microscopy (SEM), and
real-time PCR (qPCR) will be used to characterise and quantify vaccinia virus particles.
Bicinchoninic acid (BCA) assay will be used to quantify total protein and PicoGreen
will be used to quantify double stranded DNA (dsDNA). Both total protein and DNA
levels are identified as critical quality attributes (CQAs).
The final results chapter will consist of a process economics analysis that compares a
defined monolith-based chromatography process and an ultracentrifugation process
from an overall cost of goods perspective. The aim of this analysis is to further our
understanding of where the major costs and process risks from each process option are
likely to come from. Initial assumptions will need to be made but these will be tested in
a sensitivity analysis and then discussed in detail. Once complete, the model will be
able to be used for two potential purposes. The first one would be to assist in identifying
drivers for process development in a chromatographic purification process scenario. The
second purpose of this model would be to act as a decision tool to support later
discussions with Barts Cancer Institute, who are developing vaccinia virus as an
oncolytic therapy for a range of cancers, in order to manufacture some of their
constructs for clinical development. An example of a question that this model aims to
answer is “How well would a monolith process need to perform to ensure a cost
benefit over an ultracentrifugation process?”
A section on process validation will be included that aims to show how the CQAs for
vaccinia virus produced in a commercial setting for an oncolytic indication might be
defined. The challenges associated with their characterisation from the point of view of
safety and efficacy will be described and best practices discussed. The validation
challenges involved in the production of monoliths will also be presented as it is
31
thought that these will be important considerations when validating a monolith
chromatography step.
A number of challenges needed to be overcome over the course of this project. After
conducting an internal risk assessment at UCL, Vaccinia virus was classified as
requiring category 2 contwotainment. The department of Biochemical Engineering is
organised into a number of labs and pilot facilities which are designed to be shared
amongst all researchers and in some cases undergraduate and MSc students for the
purposes of teaching. For this reason it was concluded that vaccinia virus would need to
be handled in a specialist CAT II vaccine laboratory with dedicated equipment so as to
reduce any risks of cross contamination. This was built and commissioned over the
course of this project between 2012 and 2015, but it meant that a significant amount of
work needed to be undertaken elsewhere. Some of the work was performed at BIA
Separations in Slovenia. BIA Separations has access to chromatography equipment,
however, didn’t have the ability to perform non-plate based assays so analytical
measurements such as Western blots, qPCR, and NTA could not be performed. Barts
Cancer Institute did allow access to western blot facilities, however it was decided that
this additional assay didn’t add much clarity on how the system was performing as it is
only semi-quantitative as well as fairly time consuming.
Virus analytics are generally accepted as being time consuming, and imprecise meaning
that a bottleneck in terms of throughput as well as the ability to make conclusive
decisions presented itself.
32
Time was spent establishing and partially optimising TCID50, PicoGreen and BCA, as
well as NTA, qPCR, TEM, and SEM as they became available towards the end of the
project when labs at UCL became available.
Red florescence was used to confirm infectious titre (as a second read out during
TCID50 analysis).This also acted to increase the selectivity of the TCID50 assay
confirming the presence of vaccinia virus.
The aim of this project was to understand and work to reduce the challenges in vaccinia
virus purification. A monoliths capture step was evaluated and shown to be a reasonable
process option for manufacture at 100L scale.
33
Chapter 2
Introduction
2.1 Vaccinia Virus
2.1.1 Taxonomy, Classification, and Properties of Vaccinia Virus
The exact origin of vaccinia virus is not known. There are, however, a number of
theories. One is that vaccinia virus was a form of orthopoxvirus that previously infected
animals but is no longer endemic. Another suggestion is that it may have originated
from horsepox virus as at least one strain of vaccinia virus (Ankara) was isolated from a
horse (Antonio Alcami et al., 2010). Vaccinia is also said to be closely related to
cowpox (Bourquain et al. 2013).
Vaccinia virus is a member of the Poxviridae family of viruses and is a large, complex,
double stranded DNA-based poxvirus. The virus is described as “brick-shaped” with
dimensions of approximately 300 x 250 nm (Michen and Graule 2010).
The Poxviridae family of viruses is split into two additional subclasses that infect
vertebrates and insects; these are named Chordopoxvirinae and Entomopoxvirinate
respectively. Vaccinia virus, which belongs to the subfamily Chordopoxvirinae, belongs
to the genus Orthopoxvirus (Wittek 1994).
The mass of a typical vaccinia virus particle has been found experimentally by using
microscale silicon cantilever resonators and atomic force microscopy to be of ~7.9 (+/-)
4.6 femtograms (fg) and ~12.4 (+/-) 1.3 fg (Johnson et al. 2006). This data was
obtained from cantilevers of 6 µm x 4 µm and 21 µm x 9 µm respectively.
34
The isoelectric point (pI) of vaccinia virus Lister strain, which is likely to be an
important consideration when developing adsorption-based processes, was found
experimentally to range from 3.0, when using micro-electrophoesis with pure material,
to 5.1, when using isoelectric focusing techniques with more crude material (Michen &
Graule 2010). The pI of a protein is the pH in which the total net charge of the molecule
is equal. This is documented as being determined by the superposition of the protonated
and unprotonated functional groups. In the case of vaccinia, complexity is increased
exponentially as there are multiple different proteins on the viral surface, each one with
a different isoelectric point. As vaccinia virus is over an order of magnitude lager than a
typical protein such as a monoclonal antibody (mAb) (~12nm), the location of charge
densities on the viral surface will also have an effect on the adsorption characteristics of
the virus, meaning that even if the overall net charge is negative at physiological pH a
virus may still be able to adsorb to a cation exchange (CIEX) matrix.
2.1.2 Vaccinia Virus Life Cycle
The life cycle of vaccinia virus is complex, the mechanisms of which are of particular
importance both clinically and from a process development perspective when
developing oncolytic vectors.
Unusually for DNA-based virus, vaccinia replicates in the cytoplasm of a host cell, and
once mature, is referred to as Intracellular Mature Virus (IMV). At this stage the virion
is thought to be surrounded by a lipoprotein membrane (Townsley et al. 2005). As the
IMV moves away from the viral factory, transported on microtubules, it is wrapped with
a double membrane derived from the trans-Golgi apparatus and is given the name
Intracellular Enveloped Virus (IEV). The IEV is then moved to the cell surface by
35
fusing with the cell membrane, becoming referred to as Cell Associated Enveloped
Virus (CEV). This induces the formation of actin tails that act to propel the virus away
from the cell surface. At this point the virus is known as Extracellular Enveloped Virus
(EEV) (Smith et al. 2002).
The therapeutic benefits of the different viral forms are not well known. All viral forms
are known to be infectious, but there is some speculation that vaccinia virions with a
producer cell derived envelope (IEV,CEV, or EEV) might stimulate more of an innate
immune response than IMV which in turn is thought to limit the number of infectious
viral particles able to migrate to tumorigenic tissues (Ferguson, Lemoine, & Wang
2012).
2.1.3 Use of Vaccinia as a Smallpox Vaccine (1967-1980)
2.1.3.1 Background and History of Smallpox Vaccination
Edward Jenner is regarded worldwide as the initial promoter of vaccination after
publishing his work entitled “An inquiry into the causes and effects of the variolae
vaccinae” in 1798.
This work describes the use of cowpox, horsepox and attenuated smallpox to vaccinate
against endemic smallpox, which at the end of the 18th
century was recognised as a
major public health threat (Baxby 1999).
The worldwide eradication of smallpox, which was declared by the World Health
Organisation (WHO) in May 1980 (Fenner et al. 1988), is arguably the most famous use
of vaccinia virus in the 20th
century. It was a program launched in 1967 and run over 13
years, that aimed to eradicate smallpox from 31 countries around the world. According
36
to (Fenner, Henderson, Arita, Jezek, & Ladnyi 1988) it is estimated that ~300 million
people were vaccinated each year.
2.1.3.2 Obstacles in the Development and Supply Chain of vaccinia virus
as a smallpox vaccine
Due to the immense scale of the project, vaccinating such a large group of people from
all over the word using an infectious virus, the WHO had to deal with a number of
significant engineering challenges. The first was how to produce enough virus to meet
the world’s demand. The next challenge revolved around how to effectively distribute
the vaccines to hot and hard to access places across the globe without it loosing
infectivity.
In answer to the first obstacle, three main strategies of vaccine production were
established. Virus was produced within endemic countries, donations were made
directly to the WHO, subsequent to a resolution of the Twelfth World Health Assembly
in 1959 (Fenner, Henderson, Arita, Jezek, & Ladnyi 1988), and virus was supplied
directly to developing countries from bilateral aid organisations.
The second obstacle was tackled by the use of freeze-drying, a technique rediscovered
by Shackell in 1909 (Meryman 1976). It was used in place of a liquid formulation for
the New York City Board of Health (NYCBOH) strain of vaccinia virus, which was
developed in part by Wyeth® as Dryvax® (J.Michael Lane 2003). The advantages of
the freeze-dried virus preparations were that they had an increased shelf-life and could
be distributed easily without losing infectivity (J.Michael Lane 2003).
37
2.1.3.3 Production of the Smallpox Vaccine (Vaccinia Virus)
During the eradication program vaccinia virus was generated from lymph collected
from the skin of live animals, often calves and sheep, that had been infected with
vaccinia (Monath et al. 2004). Purification was then performed by using sucrose
gradient ultracentrifugation, which would typically give a percentage viral recovery of
between 33% and 63% (Joklik 1962). While quality control measures were on the
agenda of the WHO, they were not as developed as they are today. The manufacture of
vaccinia virus in the form of Dryvax® by Wyeth® caused a number of undesirable side
effects due to the presence of adventitious agents and other impurities (Monath,
Caldwell, Mundt, Fusco, Johnson, Buller, Liu, Gardner, Downing, Blum, Kemp,
Nichols, & Weltzin 2004). Adverse reactions included major cutaneous reactions such
as vaccinial skin infection, particularly in patients with eczema, as well as acute
myopericarditis (inflammation of the pericardium and heart) and encephalitis
(inflammation of the brain). This was often seen in immunocompromised patients and
patients with cardiac diseases. For this reason there has been a strong drive since this
campaign to improve the safety of viruses and other therapeutic agents by developing
well characterised and validated processes. Work has also focused on modifying
existing strains to reduce virulence and pathogenicity (Tartaglia et al. 1992).
38
2.1.4 Use of vaccinia as an oncolytic and immunotherapy vector
2.1.4.1 Background to Oncolytic and immunotherapeutic viral therapies
An oncolytic virus is a virus that is known to infect, replicate in and lyse tumour cells
preferentially to normal cells. Oncolytic viruses are also thought to be able to stimulate
anti-tumour specific immune responses against certain antigens produced by cancer
cells.
The immunogenicity of cancer cells (their ability to stimulate an adaptive immune
response) varies between cancer types and also among individuals. It is thought likely
by Pierre G. Coulie (Blankenstein et al. 2012) that all tumours cells present antigens
that can be recognised and targeted by T-cells. However certain cancers, one example
being melanomas, produce antigens that trigger a more elicit immune response than
antigens specific to other cancer types. This allows for the development of more
effective immunotherapies.
It has been postulated in the past that differences in immunogenicity of cancer cells has
to do with their expression of self and non-self-antigens. Self-antigens are antigens that
are normally produced by healthy cells in the body while non-self-antigens are antigens
specifically produced by tumour cells. More recently, however, this theory has been
challenged, and (Pradeu and Carosella 2006) describe an alternative. This is that it is not
the difference in an antigen’s origin that impacts its immunogenicity, but in fact
differences in the molecular structure of the antigen’s epitope. They put forward the
view that epitope structures that are significantly different to those that the immune cells
regularly interact with are more likely to elicit an immune response. This is potentially
significant as a non-self-antigen may be able to fool the immune system by mimicking
epitope structures of self-antigens.
39
A number of monoclonal antibodies acting as vaccines and immunomodulation agents
against cancer have already been approved by the FDA, but it is widely accepted that
combinatorial immunotherapies and more effective delivery platforms need to be found
if the treatment of cancer by immunotherapy is going to be successful.
2.1.4.2 Recent Advances in Oncolytic Therapeutics
A number of viruses have been considered for their potential oncolytic applications. It
has become relatively well known that viruses such as vaccinia (Hung et al. 2006),
reovirus and Newcastle disease virus (McCart et al. 2001), as well as human papilloma
virus (HPV) and Epstein-Barr virus (EBV) (Blankenstein, Coulie, Gilboa, & Jaffee
2012) can infect and replicate in tumour cells preferentially to normal cells. In addition,
herpes simplex virus type 1 and adenovirus can be engineered to be less pathogenic to
healthy human cells and more tumour specific, suggesting that they could be developed
into successful oncolytic agents (Ferguson, Lemoine, & Wang 2012), although
oncolytic adenovirus has a number of limitations, most notably being poor spread
through tumour cells (Shayakhmetov et al. 2002).
The first oncolytic therapy, Oncorine, was licenced in China for the treatment of head
and neck cancer in November 2005, and produced by Shanghai Sunway Biotech. This
virus is a genetically modified recombinant human adenovirus 5 (AdV) (Garber 2006).
While this has been identified as a fantastic and encouraging result for the field, Ken
Garber outlines that further work is needed in order to convince the rest of the
developed world to invest in the same strategies in his review article “China Approves
World’s First Oncolytic Virus Therapy For Cancer Treatment” (Garber 2006). It
appears that the Chinese regulators based their approval on objective response rate
40
rather than overall patient survival. This was made difficult owing to the fact that a
number of patients lived in rural places and were never revisited by doctors once initial
responses were recorded. This is recognised as a “weakness in the clinical trial” by the
company’s president and plans to report survival rates in subsequent trails, as well as an
intention to follow up with a number of patients from the AdV trail has been stated by
Shanghai Sunway Biotech (Garber 2006).
More recently T-Vec or Imlygic® licenced by Amgen, Inc. became the first oncolytic
viral therapy licenced in the US. T-Vec was derived from human herpes simplex virus
type 1 and was licenced on 27th
October 2015 by the US food and drugs administration
(FDA) as an oncolytic virus for use in melanoma patients with injectable but non-
resectable lesions in the skin and lymph nodes (Pol et al. 2016).
2.1.4.3 Obstacles to the Systemic Delivery of Oncolytic Viral Therapies
The delivery of oncolytic viruses by intravenous injection has a number of potential
advantages. A viral therapy that can be injected intravenously has the potential of being
able to reach distant metastasis as well as primary tumours. Intratumoural injection of
oncolytic virus has shown some success in targeting solid tumours; however, it is well
understood that death from cancer is often due to metastatic disease or recurrence,
especially in distant hard to reach tumours, for example in the brain or pancreas
(Ferguson, Lemoine, & Wang 2012).
One of the major challenges is that host defences limit the ability of viruses to infect
tumour cells, particularly when injected intravenously. These include the activation of
complement, neutralising antibodies and antiviral cytokines. There has also been
reported uptake of virus by tissues and macrophages, all which limit the ability of the
41
virus to remain in the bloodstream and target distant metastasis (Ferguson, Lemoine, &
Wang 2012;Wong et al. 2010). The limited ability of certain viruses to trigger a tumour-
specific immune response can also be a limiting factor (Pol, Kroemer, & Galluzzi
2016). The limitations of systematic delivery of oncolytic viruses can be observed in
Figure 2.1.
Figure 2.1: Illustration to show hurdles encountered by the systemic delivery of oncolytic
viruses (Adapted from (Ferguson, Lemoine, & Wang 2012)). This shows that the action of
blood vessels, neutralising antibodies, and uptake by organs can reduce the amount of
virus available for uptake by tumour cells.
2.1.4.4 Clinical Applications of Vaccinia Virus
There are a number of reasons why vaccinia virus has been identified as a good
candidate for oncolytic therapy (Kirn & Thorne 2009). One reason is that there is a lot
of both laboratory and clinical data available on vaccinia virus biology and host
response profiles to infection, following the use of multiple vaccinia virus strains during
the smallpox eradication program. Commonly used strains for therapeutic use include
the New York City Board of Health (NYCBOH), Lister, Copenhagen, Western Reserve,
42
Tian Tan, Modified Vaccinia Ankara (MVA), and NYVAC (derived from the
Copenhagen strain) (Kirn & Thorne 2009). It should be noted that these vary in their
oncolytic potential.
Another reason is that vaccinia has been shown to be very tumour selective, this is due
in part to the fact that tumour cells produce less interferons (IFN) than normal cells in
response to vaccinia virus infection (Luker et al. 2005). Vaccinia virus has also been
shown to have a high host range and tissue tropism, allowing for broad spectrum tumour
specificity (Tysome, Briat, Alusi, Cao, Gao, Yu, Wang, Yang, Dong, Wang, Deng,
Francis, Timiryasova, Fodor, Lemoine, & Wang 2009).
Throughout the past decade, genetic engineering has been used to increase viral safety
profiles and enhance clinical efficacy. One example of a genetic modification used in
pre-clinical trials using the Western Reserve strain of vaccinia virus is the deletions in
both the thymidine kinase (TK) and vaccinia growth factor (VGF) genes. This was
found to activate the transcription factor E2F and the Epidermal growth factor receptor
pathway (EGFR pathway), which in turn triggers the oncolytic effect (Kirn & Thorne
2009).
The virus was also engineered to express granulocyte-macrophage colony-stimulating
factor (GM-CSF) which was shown to stimulate viral attack of foreign tumour cells.
This reduced the potency of vaccinia virus in normal cells while increasing it in tumour
cells (Thorne et al. 2007).
A different approach using the Lister stain of vaccinia virus for the treatment of
pancreatic cancer is the insertion of a combined endostatin-angiostatin fusion gene
(Scappaticci et al. 2001) which, when inserted in combination, has been shown to act
synergistically to inhibit angiogenesis (Tysome, Briat, Alusi, Cao, Gao, Yu, Wang,
Yang, Dong, Wang, Deng, Francis, Timiryasova, Fodor, Lemoine, & Wang 2009). This
43
approach has also been demonstrated in head and neck squamous cell carcinoma
(HNSCC) models in vitro as well as in vivo (Tysome, Wang, Alusi, Briat,
Gangeswaran, Wang, Bhakta, Fodor, Lemoine, & Wang 2011).
The potential therapeutic benefit of oncolytic viral vectors is undoubtedly very high. It
is clear that a significant amount of work is still required in order to find viral strains
with the right genetic modifications to deliver optimal therapeutic outcomes, but these
medicines will also need to be affordable. This will require the development of
commercially scalable and cost-effective processes early on in clinical development.
2.2 Options and Considerations When Developing Viral Bioprocesses
2.2.1 Introduction
In the early 20th
century, from around 1900 until 1966, the smallpox vaccine (vaccinia
virus) was produced from the lymph of calves and sheep. In the 1920’s the concept of
bacterially sterile cell culture preparations was starting to become better understood and
legislated. The large number of vaccinia doses required for the eradication of smallpox,
however, meant that the majority of vaccines were not able to be produced in sterile
culture, even though the technology was available (Fenner, Henderson, Arita, Jezek, &
Ladnyi 1988). The result was that viral preparations were often contaminated with
bacteria and toxins that could not be completely removed by technologies such as
ultracentrifugation. This increased the incidence of adventitious side effects.
44
2.2.2 Cell Culture and Primary Recovery
The majority of modern viral therapies including vaccines are now produced in either
chicken eggs, or cell culture, with common cell lines being human, monkey, avian and
insect cells. With the advancement of modern adherent cell culture technologies, egg-
based manufacturing is usually not the preferred option. This is due to costs associated
with scaling out, as well as environmental and animal welfare concerns. There are also
risks to manufacturing resulting from avian flu and other diseases. Having said this, for
the manufacturing of influenza vaccine, egg-based production is used for the majority of
vaccines manufactured globally (Soema et al. 2015).
Many viral cell lines are adherent, and will only grow as monolayers on solid surfaces.
Technologies currently used for culture of adherent cells with their pros and cons are
listed in Table 2.1
45
Table 2.1 List of currently available technologies for expanding adherent cell lines with pros, cons as well as an example of commercially available systems
(table not exhaustive)
Technology Pros Cons Example
T-Flasks
Roller -
Bottles
Cell stacks
- Good productivity
- Easy to handle in small volumes
- Flexible
- Can recover cells easily
- Do not require cell line development
- Limited scale up capacity
- Not well controlled or automated
- Hard to run in fed batch mode
Corning® Cell
Culture Flasks
Packed Bed Bioreactors - Scales up to ≥ 500 m2
- Can be run in fed batch or perfusion mode
- Fully intergraded controls
- Does not require cell line development
- Poorly characterised small scale
systems
- Inherent variability in packed bed
characteristics
- Cannot recover cells easily
iCellis 500 (Pall Inc)
Hollow Fibre Bioreactor - High surface area to volume ratio (100-200
cm2/mL
- Can be run at low sheer for longer process
times
- HF designed now well characterised in
terms of porosity and uniformity
- Well characterised small scale units
- Can be run in Fed batch or perfusion
- Fully intergraded controls
- Max surface area limited to 2.5 m2
(lower than the iCellis)
- Difficult to harvest cells (although
easier than iCellis)
- May require cell line development
FiberCell cartridges
(FibreCell System
Inc)
Micro carriers - Easily scalable
- Large surface area achievable
- Fully integrated controls
- Can run in fed-batch and perfusion reactors
- May require cell line development
- Hard to recover cells
- Possible productivity limitations
Cytodex
GE Healthcare
46
For therapeutic areas such as some cancers, or genetic disorders affecting muscular
tissues (an example being Duchenne Muscular Dystrophy), the amount of therapeutic
virus needed to be produced is considerable. Some examples of technologies designed
to scale up adhering cells are shown in Table 2.1; however, challenges to large scale
production are a massive driver to adapt commonly used cell lines such as CV-1, Vero,
or HEK293 cells to grow in suspension. The reasons for this are that adherent cell
culture technologies will always be limited by scale, therefore affecting overall process
productivity. It can also be very difficult with many technologies to recover the cells
which, especially for purposes of in-line monitoring, makes robust process control and
characterisation difficult. The major disadvantage of adapting an adherent cell line to
grow in culture, however, is that it requires extensive cell line development which in
turn requires time, resources and significant expertise.
Infectious viral particles in culture have been shown to be concentrated both inside the
cell, therefore requiring cell lysis, and in the supernatant. Specific examples are vaccinia
virus (Smith, Vanderplasschen, & Law 2002), which has a number of viral forms that
are concentrated inside the cell, and murine leukaemia virus (MLV) (Aboud et al.
1982). As a rule of thumb, it is often seen that non-enveloped virus such as AAV are
concentrated inside the cell, whereas enveloped viruses such as Lentivirus are
concentrated in the supernatant (Segura et al. 2011). If the virus is concentrated in the
supernatant, the main challenge becomes removing the cells whilst maintaining high
yields of infectious virus. It can also be a challenge to remove host cell proteins, DNA,
lipids, and other process-related impurities.
If the virus is located intracellularly, then cellular disruption is required in order to
release the virus. Perhaps the most common laboratory scale method for cell disruption
is multiple freeze thaw cycles of pelleted cells post centrifugation. At large scale,
47
however, this becomes impractical and expensive. Scalable methods include hypotonic
shock, chemical lysis, ultra-sonication and mechanical disruption. Mechanical
disruption by homogenisation is arguably the method of choice due to its ease of
scalability and reproducibility (Monath, Caldwell, Mundt, Fusco, Johnson, Buller, Liu,
Gardner, Downing, Blum, Kemp, Nichols, & Weltzin 2004). Changes in pressure and
high shear forces can be produced and controlled to break open cells without damaging
the viral particles, although this will likely depend on the viral size liability. An
example of the use of homogenisation can be found in (Joklik 1962). Alternatives are
agitation with glass beads as described in (Zwartouw et al. 1962).
In terms of cell removal, common methods include centrifugation and microfiltration.
Flocculation, precipitation and aqueous two phase extraction, some of which will be
described in more detail later in this chapter, have been attempted but are not common
practice. Centrifugation is a common primary recovery unit operation in protein
manufacturing due in part to its ease of scalability (Maybury et al. 2000). As many
oncolytic and gene therapy products are currently produced at relatively small scale,
microfiltration unit operations are discussed more in relation to viral primary recovery
processes. Companies such as EMD Millipore and 3M have commercialised a range of
depth filters which they have suggested can be used generically for viral bioprocesses.
Application notes are available showing the use of depth filters as a primary recovery
step for cell culture-based influenza vaccine processes (EMD Millipore 2014).
Membrane filters of differing pore sizes can be run in both dead end and tangential flow
filtration (TFF) mode. An example of membrane dead end filtration can be found when
manufacturing cGMP lentiviral vectors (Ausubel et al. 2012). Examples of TFF
filtration applications in viral primary recovery and as a viral concentration step can be
found in Section 2.2.3.2.
48
2.2.3 Purification
2.2.3.1 Ultracentrifugation
The gold standard unit operation for the purification of viral particles is
ultracentrifugation, used throughout the 20th
century for the purification of multiple pox
viruses (Zwartouw, Westwood, & Appleyard 1962). Ultracentrifugation can be broken
down into two different modes (Segura, Kamen, & Garnier 2011). The first is
equilibrium density ultracentrifugation, and the second is rate zonal ultracentrifugation.
Equilibrium density ultracentrifugation uses a high density bed of sucrose, caesium
chloride, (CsCl), iodixanol, Percoll®, or Nycodenz
® to separate virus material based on
its buoyant density. Rate zonal ultracentrifugation separates based on size and density.
Density gradient ultracentrifugation works by loading virus either manually into a
stationary ultracentrifugation tube containing a high density bed as described above, or
continuously into the bottom of a packed rotating rota, the latter being more common at
large scale. Rotational speeds can reach ≥ 40,000 rpm, with a resulting relative
centrifugal force (RCF) of ≥ 100,000g, The duration of the process can last from many
hours to even days, while particles move down the bed from an area of low
concentration of media to an area of high concentration until the buoyant density of
each particle reaches that of the media. Rate zonal ultracentrifugation on the other hand,
while similar, can be performed more rapidly. A smaller, often more concentrated
volume of virus is loaded onto the top of a gradient bed, which is again rotated at high
speeds. While the virus will still band once it reaches its buoyant density if left to settle
for long enough, the different particles present in the sample will travel down the bed at
differing velocities depending on their size as well as (to a lesser extent) any differences
in density and overall viscosity. In order to take advantage of this phenomenon, the
ultracentrifuge is stopped prior to density equilibrium being reached, so that each
49
volume fraction can be analysed. This can be a high resolution technique used for viral
purification due to the difference in size between virus particles and common
impurities. However, as sample volume needs to be extremely small (~10% bed
volume) scale up is challenging. At bench scale, typical bed volumes scale to ~250 mL.
When using density gradient ultracentrifugation, vaccinia virus will often require
around 12 hours (typically overnight) to band; smaller viral particles such as AAV can
take up to 3 days. The operation is typically performed at 2-8⁰C to reduce the risk of
viral inactivation.
The general disadvantages of density gradient ultracentrifugation are that the technique
is very time consuming, requires skilled operators and is very labour intensive. At
production scale it commonly takes two operators to setup and clean the system. This,
coupled with the fact that in the past recovery of infectious viral particles was shown to
be low, between 33% to 63% being fairly liberal estimates (Joklik 1962), demonstrates
the need for a more optimal and scalable purification processes for viruses.
There are several companies that produce density gradient ultracentrifuges for
production scale processing. A relevant example is Alfa Wassermann which
manufactures a KII ultracentrifuge that has a maximum process capacity of 200 L (Alfa
Wassermann 2013) (Figure 2.3).
Density gradient ultracentrifuges are not run in the same way from bench to
manufacturing scales. Bench-scale ultracentrifuges are designed to be run in batch mode
while larger manufacturing scale ultracentrifuges are often run in a semi-continuous
mode. At bench-scale, an ultracentrifuge tube is filled with a packing matrix such as
sucrose at differing concentrations from top to bottom. High concentrations are loaded
into the tube first and then lower concentrations are added on top. This produces a
sucrose concentration step gradient from the top to the bottom of the tube. Alternatively,
50
instead of sucrose, salts such as caesium chloride can also be used. These form linear
concentration gradients when run under high centrifugal forces due to the high density
of caesium salt (CsCl). In both cases, virus is loaded into the top headspace of the tube,
which is then placed into the ultracentrifuge rotor and rotated at high g force (~RCF x
100,000).
Once the run is complete, the tubes are removed and harvested using a sterile needle
which is used to pierce the bottom of the tube. Fractions are then removed and analysed
for viral particles. This process is time consuming and prone to human error.
Semi-continuous ultracentrifugation works slightly differently to batch
ultracentrifugation as shown in Figure 2.2.
Figure 2.2 A graphical representation taken from (Sandra Merino 2015) to show how
large scale ALFA WASSERMAN KII ultracentrifuges work.
51
Each step (1-7) as indicated in Figure 2.2 is listed below:
1. Ultracentrifugation gradient matrix is pumped into a stationary rotor (maximum
volume 3.2 L)
2. Rotor is slowly accelerated and the bed aligns vertically on the walls.
3. Virus material is loaded into the bottom of the rota and particles are captured at
the top of the bed – towards the middle of the rotor. Effluent flows out the top of
the rota.
4. Viral particles move down the bed from the centre of the rota to the outer walls
until their buoyant density is equal that of the media.
5. The rotor is decelerated until stationary.
6. The bed reforms horizontally but the gradient remains in place.
7. Fractions are pumped out the bottom of the rotor and passed through a UV cell
to determine where virus has banded. Fractions can also be sampled for further
analysis.
One of the major limitations of ultracentrifugation is volumetric throughput. Bench
scale centrifuges are limited to a few hundred millilitres. Large scale ultracentrifuges
can process more material, but this is still limited to a maximum of ~200L depending on
the virus being purified. According to Alfa Wassermann this can be as low as 5 L for
Hepatitis B, while in the case of influenza process volumes can reach up to 150 L. After
a run the bed is collected in fractions between 15 and 500 mL. Each fraction is analysed
for infectious virus particles. There have been some issues reported when using
ultracentrifugation related to the removal of genomic DNA, which has a similar density
to many viral particles. Alfa Wassermann suggests the use of benzonase if this is found
to be the case; however, it should be noted that this is an expensive processing option as
52
nucleases and assays to test for their removal are costly and require validation. (Alfa
Wassermann, 2013).
Another possible disadvantage of ultracentrifugation is that the machines can be
difficult to clean as they need to be taken apart manually. It can take two operators,
working together, several hours to clean and shut down a single system. This means that
even if several machines are used in parallel in order to process a large volume of
material, a bottleneck in terms of time and labour is experienced.
Ultracentrifuge rigs are also very expensive with prices greater than 300,000 USD for a
process scale rig. Costs associated with ultracentrifugation will likely increase further
when validation qualification and infrastructure requirements for GMP use are taken
into account.
Figure 2.3 Picture of a KII Ultracentrifuge from Alfa Wassermann (Sandra Merino 2015).
53
There are a number of challenges to large scale ultracentrifugation making many
industrialists keen to find alternatives. What is important to keep in mind, however, is
that ultracentrifugation also has some advantages which can be difficult to find in other
emerging technologies; these, together with some disadvantages, are summarised in
Table 2.2.
Table 2.2 Advantages and disadvantages of ultracentrifugation
Advantages of Ultracentrifugation Disadvantages of Ultracentrifugation
- Not reliant on changes in pH or salt
concentrations to generate a
separation. These can inactivate
some viruses
- Low sheer environment
- Well tried and tested technique
requiring little process development
- Rotas can be steam sterilised
- Some processes give high
percentage recovery and purity
- High capital expenditure
(CAPEX) and labour costs
- Relatively low throughput -
difficult to scale up
- Can be very manual and
laborious
2.2.3.2 Alternatives to ultracentrifugation
There are a number of scalable alternatives to ultracentrifugation. Comparison studies
dating back to the 1980’s have shown that many can outperform bench scale
ultracentrifugation.
The issue is that in order to replace ultracentrifugation, in which significant investments
have already been made, results from other technologies need to show significant
54
improvements in purity and yield as well as productivity. Techniques such as
chromatography, aqueous two phase and precipitation are not straightforward to
develop. Complex multifactorial experiments need to be performed in order to generate
process knowledge and understanding. This is not the case with ultracentrifugation, or
with TFF. Many factors that affect the separation efficiency of an ultracentrifuge cannot
be modified; therefore relatively little process development is required. Experienced
operators are needed to run manufacturing campaigns, and a significant amount of
capital is required to purchase equipment, but once these are in place, there is often not
much of a driver to switch technologies.
The main and most commonly developed alternatives to ultracentrifugation are
summarised in Table 2.3.
55
Table 2.3 Alternatives unit operations to ultracentrifugation
Unit Operation Viral purification applications References
Tangential Flow
Filtration
(TFF)
- Both flat sheet (FS) and hollow fibre (HF) filters have
been used to concentrate and purify viral feed streams
and can be used to remove cells, cell debris, proteins, and
DNA from crude lysate.
- Relatively low molecular weight cut-off (small pore)
filters are used to remove proteins and DNA.
- Large pore diameter filters are typically used for the
removal of cells and larger debris.
- Filters are commonly manufactured using either
polyethylene sulfone (PES) or regenerated cellulose
(RC).
- Nominal channel diameters range from <100KD to
>0.45µm
- One potential drawback is that shear forces need to be
well controlled to prevent membrane fouling and viral
degradation. This can be easier to control and measure
when using HF filters rather than FS.
- Flux vs TMP studies are required during process
development in order to generate understand of critical
process parameters (CPPs).
- In general TFF is considered as a relatively low
resolution technique, but easily scalable to large process
volumes.
(Negrete et al.
2014;Wickramasin
ghe et al. 2005)
Gel Filtration
- Size exclusion chromatography also known as gel
filtration (GF) has been shown to achieve higher yields
than density gradient ultracentrifugation, although
sometimes struggles to remove large contaminants such
as genomic DNA.
- Typical issues associated with GF often relates to
volumetric throughput. Good separation requires low
load volumes, typically ~2% of the total column volume.
- Due to load volume restrictions GF is often found
towards the end of the process once volume is reduced.
- GF benefits by allowing isocratic elution often in
physiological buffers such as PBS.
- In general GF is regarding as high resolution, low
volume technique.
(Andreadis et al.
1999;McGrath et
al. 1978)
Precipitation
- Precipitation using PEG, ammonium sulfate and calcium
phosphate has been used to aggregate viral particles
allowing removal by low speed centrifugation.
- Variable recovery of infectious viral particles has been
reported in the literature likely due to changes in osmotic
pressure. Reported for Retrovirus, but thought likely to
affect other particles.
- A combination of PEG 6000 followed by Sepharose Cl-
4B (GF) has been reported to achieve a good recovery
and purification efficiency of both extracellular and
intracellular moloney murine leukemia virus. This was
reported as being especially true for larger volumes as
the PEG 6000 was able to pellet virus from bulk fluid
prior to chromatography.
- Potential issues regarding large scale manufacturing are
solubilising viral pellets formed, and removal of co-
precipitated impurities.
(Morenweiser
2005) (Aboud,
Wolfson, Hassan,
& Huleihel 1982)
56
Unit Operation Viral purification applications References
- The mechanisms of viral precipitation are generally not
well understood. This leads to challenges in process
development.
Aqueous Two
Phase (ATPS)
- Aqueous two phase has been attempted with chemicals
such as PEG, dextran and polyvinyl alcohol to separate
virus material from cellular components, An example
being a dextran-polyethylene glycol system successfully
used to concentrate and purify HIV-1 along with external
gp120 protein.
- Scale up is generally considered possible using
convention extraction equipment used in the chemical
industry, however requires investment.
- ATPS requires a lot of process development as phase
equilibrium and protein partitioning is not well
understood
- Raw material costs (polymers) is also a concern.
(Morenweiser
2005) (HAMMAR
and GILLJAM
1990) (Cunha and
Aires-Barros
2000)
Chromatography
- Packed bed systems and continuous polymer monoliths
and membrane sheets have been used successfully to
purify a range of viral particles for clinical use.
- Common types of chromatography used include Affinity
(AF), Cation and Anion exchange, (IEX), Hydrophobic
interaction, (HIC), Immobilised metal affinity (IMAC) as
well as Pseudo-affinity and mixed modal resins.
- Use of chromatography to purify viruses is discussed in
more detail in Section 2.3.
(Cheng et al.
2001;Gagnon
2008;Wolff et al.
2010b)
New generation chromatography using membranes, fibres, and monoliths are perhaps
the most widely published alternatives to ultracentrifugation for capture and purification
of viral particles. Advances in resin design have paved the way to some very promising
process designs with some impressive results. The next section of this introduction will
concentrate on chromatographic history, theory, applications and results.
57
2.3 Chromatography
2.3.1 Overview
Chromatography was first developed by the Russian botanist M.S. Tswett, who has
often been described as the father of modern chromatography. He published his first
paper in 1903 on the separation of xanthophylls and chlorophyll on an inulin column.
His second paper in 1906, on the insolubility of chlorophyll in petrol and ligroin,
resulted in an early description of adsorption (Ettre and Sakodynskii 1993a;Ettre and
Sakodynskii 1993b). Chromatography can, in general terms, be described as “a
separation procedure for resolving mixtures and isolating components” by a process of
“differential migration”(Doran 2005).
Chromatography has been used for the separation of a large number of biological agents
from proteins, vitamins, and amino acids, to viruses, virus-like particles (VLPs) and
plasmid DNA (pDNA).
A solvent otherwise known as the mobile phase, typically a liquid in liquid
chromatography or a gas in gas chromatography, travels down a column and interacts
with a stationary phase at different rates depending on its affinity for the matrix (Doran
2005). This is traditionally a solid support, although it can also be a liquid in liquid-
liquid chromatography.
For most preparative bioprocess applications, the stationary phase consists of a solid
inert compound. These structures are typically either porous beads, of ~40-100 µm,
which have small micropores of around 100 nm on their surface, or solid porous
materials with highly crosslinking channels. These channels can have very large
diameters ranging from 0.4 to 100 µm, and are therefore often used for separation of
58
large macromolecules such as viruses and virus like particles (VLPs) or even cells
(Dainiak et al. 2006).
Examples of typical bead materials, often used for protein purification, are highly cross
linked agarose (MabSelect SuRe LX®, GE Healthcare Life Sciences), polystyrene
divenyl benzene (POROS ® Applied Biosystems), polyacrylamide, (Bio-Gel, Bio-Rad),
and hydrophilic cross-linked vinyl polymer (Toyopearlm TosoH Biosep).
There are a number of single rigid porous chromatographic structures on the market.
The most common are membrane adsorbers and monoliths.
An example of a membrane that was commonly used in bioprocessing, although now
discontinued, was ChromaSorb (EMD Millipore, Darmstadt, Germany) made from
polyethylene. Another example is Sartobind®
(Sartorius, Gottingen, Germany) which is
made from stabilised reinforced cellulose. This product is available as a strong CIEX
with sulphonic acid ligands, a strong anion exchange (AIEX) with quaternary
ammonium ligands, and a HIC that uses a phenyl ligand. A salt tolerant addition,
Sartobind STIC®
designed for HCP, DNA and endotoxin removal is also available, and
it consists of primary amine ligands. The other main membrane adsorbers on the market
are Mustang filters (Pall, NYC, USA). from which Mustang S and Q are both made
from polyethersulfone (PES) and come with sulphonic acid and quaternary ammonium
ligands respectively.
Convective interaction media (CIM) monoliths are also single rigid open channel
chromatographic structures commercially made at large scale from
poly(glycidylmethacrylate-co-ethyleneglycoldimethacrylate, (BIA Separations,
Ajdovscina, Slovenia). In general, charged, hydrophobic, affinity or active groups are
chemically synthesised on the surface of the solid methacrylate support and interact
with solutes via hydrophobic or electrostatic interactions, hydrogen bonding and/or Van
59
der Waals forces. The two modes of adsorption-based chromatography to be discussed
in this thesis are preparative ion exchange and hydrophobic interaction chromatography.
2.3.1.1 Ion exchange chromatography (IEX)
In ion exchange chromatography adsorption is initiated by electrostatic interactions
between a solute and the stationary phase. Ion exchange columns can be either
positively charged, known as anion exchangers (AIEX), or negatively charged, known
as cation exchangers (CIEX). Typical AIEX ligand chemistries include quaternary
amine groups (QA) or diethylaminoethyl groups (DEAE). These chemistries represent
strong and weak anion exchanges respectively. Primary amines are also common,
although in preparative AIEX they are more often used for single use negative capture
polishing steps, which are designed to capture residual impurities such as HCPs and
DNA.
Common strong CIEX ligands are sulfate groups (SO3) in the form of sulphonic acid or
sulfopropyl groups. Weak CIEX ligands often consist of carboxymethyl groups (CM)
Separation is dependent on the difference in net charge between solute molecules, and
the subsequent affinity of the solutes to the adsorbent. pH and ionic strength can be
manipulated to bring about adsorption and elution depending on the isoelectric point of
the solute of interest.
Increasing the salt concentration (often a neutral salt such as NaCl) allows smaller
charge dense ions to compete with the often larger solute molecules for binding sites.
60
2.3.1.2 Hydrophobic interaction chromatography (HIC)
Hydrophobic interaction chromatography relies on the interaction between hydrophobic
patches on the surface of a solute and the often non-polar ligands on the stationary
phase. Butyl or phenyl groups are commonly used as preparative ligands, although
hydroxyl and epoxy groups can also be used if a weaker interaction is desired.
Adsorption is thought to be driven by an increase in entropy as a result of a reduction in
the Gibbs free energy of the solute in solution. This occurs by increasing the
concentration of kosmotropic salts such as ammonium sulfate in the load material. In
addition to this, hydrophobic amino acids present close to the surfaces of proteins are
thought to form surface hydrophobic cavities, due to the reduction in free water
molecules. These cavities are then able to bind to hydrophobic ligands. A reduction in
salt concentration and changes in pH are typically used to bring about elution, although
organic solvents are sometimes required.
2.3.2 Practical considerations when developing viral chromatography
steps
2.3.2.1 Trade-offs when evaluating process performance
A common trade-off needing to be considered when developing chromatographic
processes for the majority of products is “recovery versus purity”.
In order to increase the optimum performance of a process, both the percentage
recovery of the product of interest as well as the removal of impurities need to be
maximised. It is here that process developers come across a common problem. When
factors influencing process performance are varied, whether numerically or
61
categorically, optimum conditions favouring one response or the other are often found
rather than both together. A compromise between the two responses thus becomes
unavoidable. As a result, the relationship between each individual individual factor and
overall process performance is often nonlinear. It is also common that areas of local
optima can be found in a design space. This can be helpful in giving process developers
more flexibility, especially when considering scale up challenges to achieving a certain
criteria, since not all factors are as easy to accomplish at large scale as they are at bench
scale, but can also make the determination of global optima difficult.
When developing processes for infectious viruses, the complexity of the problem
increases further. Trade-offs relating to infectious viral processes need to take viral
infectivity into account as well as total particle recovery. Conditions that are able to
recover all viral particles loaded without any impurity contamination would only be
acceptable if the viral particles recovered were infectious.
For this reason viral stability during processing and storage is of paramount importance
and needs to be characterised. In order to do this, stability studies need to be performed
over time at a range of temperatures and conditions that best represent those of a
process.
Many viral particles are thought to be labile, although the exact mechanism of
inactivation is not described in the literature in detail. Viral sample handling needs to be
planned with stability data in mind. If viral infectivity crashes at room temperature for
example, then samples will need to be put into controlled temperature storage as soon as
possible after a fraction has been collected.
Sample handling needs to be consistent so that researches can rule out the possibility
that any loss in infectivity is the result of a variation in the experimental protocol.
62
2.3.2.2 Resin screening considerations
Both ion exchange and hydrophobic interaction chromatography have been used to
purify viral particles (Banjac et al. 2014;Gerster et al. 2013a;Opitz et al. 2008;Wolff et
al. 2010a). What is interesting is that many viruses, especially enveloped viruses, have
been shown to bind to both CIEX and AIEX at either side of their supposed isoelectric
points (Banjac, Roethl, Gelhart, Kramberger, Jarc, Jarc, Strancar, Muster, & Peterka
2014;Wolff, Siewert, Lehmann, Hansen, Djurup, Faber, & REICHL 2010b). This has
been demonstrated in protein separations in the past; however it appears to be
particularly commonly in viral chromatography. This is perhaps due to the size and
complex physicochemical properties of viral particles (Yamamoto and Ishihara 1999).
For this reason, both strong and weak CIEX, AIEX, and HIC resins should be included
in initial screening experiments.
There are a number of bottlenecks that need to be considered when setting up extensive
screening experiments. Virus process development takes time, one of the major time
bottlenecks being the infectivity assays. Assays can take anywhere from several days to
several weeks to run; so unless high throughput screening technologies can be utilised,
it is imperative to design experiments in a sequential manner in order to give as much
information as possible per experiment. It can be difficult to generate enough virus
material to perform extensive screening studies, thus experimental designs need to take
this into account. The goals of screening studies in viral bioprocessing need to be
clearly defined from the offset.
One of the attributes of using chromatography is that there are a lot of factors that can
be manipulated in order to maximise the productivity and separation achievable. The
63
advantage of this is that there is a lot that can be done to optimise a separation process
should this be required. Examples are changes in pH, salt concentration, type of buffer
system in the load, wash and elution buffers, stationary phase and ligand chemistry, and
type of matrix. The matrix could be a packed bed, monolith or membrane as previously
mentioned.
When dealing with complex feed streams such as viruses, this can also be a
disadvantage as to fully optimise, even a single step, can require a very large number of
runs. In practical terms, it is impossible to screen every possible factor that might
influence a separation. Therefore factors to be considered need to be prioritised based
on previous experience, theoretical understanding and considerations, published
literature, and often finite resources.
The use of multifactorial designs such as DoE and Simplex analysis can be useful, and
have been used successfully in the past to find areas of optima within complex design
spaces (Konstantinidis et al. 2016). Variability in viral analytics and time taken to
analyse virus samples, however, make both of these approaches challenging.
The best process development techniques to utilise for viral separations will vary
between laboratories and will not only depend on the product, but also on the situational
constrains such as time available for process development, and equipment availability.
For this reason a general statement on this topic is of limited use. A lytic virus such as
vaccinia (which requires infectivity assays to determine viral titre, being developed in a
laboratory with many other projects and with no high throughput capabilities), will need
to consider DoE and intelligently designed one-factor-at-a-time approaches rather than
high throughput screening (HTPD) and simplex analysis. On the other hand, a
laboratory optimising an AAV separation, which requires qPCR as its major
64
quantitation assay, may well be able to utilise HTPD and Simplex methodologies as
well as DoE if the resources are available.
Process developers need to take into account the amount of complexity inherent in the
feed stream, time required to asses product concentration and impurity profiles, and
assay variability.
As described later in this thesis, stability data, refined experimental design and the use
of a large variety of orthogonal analytical methods can be used to generate reasonably
robust conclusions on process performance. However, chromatographic process
development is likely to be more time consuming and expensive compared to other
techniques such as ultracentrifugation.
2.3.3 Use of chromatography for virus and other macromolecular
separations
2.3.3.1 Membrane adsorbers and packed bed columns
Membrane adsorbers have been used to purify viruses and other large macromolecules.
They have some of the same advantages as monoliths in terms of mass transfer and flow
independent adsorption; however, the achievable resolution is not as high due to a
shorter bed length and a less interlinking channel structure.
Figure 2.4 is an illustration of the comparative strengths and weaknesses of membrane
adsorbers (MA), monoliths and packed bed columns for viral separations. As it can be
seen in Figure 2.4, membrane adsorbers benefit from high capacity and flow rate, but
limited resolution. Packed bed columns can achieve high resolution, but need to be run
65
at lower flow rates and cannot achieve as a high capacity as membranes or monoliths.
Monoliths benefit from being able to run at high flow rates without compromising
capacity or resolution.
Figure 2.4 Visual comparison between packed bed, membrane adsorbers and monolith
chromatography, adapted from (Ales Strancar 2011).
There have been a number of studies that have used membrane adsorbers to purify
viruses such as vaccinia, influenza, retroviral vectors, adenovirus and others (Peixoto et
al. 2008;Wolff and Reichl 2008;Wolff, Siewert, Hansen, Faber, & Reichl 2010a).
(Wolff, Siewert, Hansen, Faber, & Reichl 2010a) describe the use of pseudo-affinity
membrane adsorbers (MA) combined with HIC resins from the ToyoScreen HIC mix
pack (Tosoh Biosciences GmbH, Stuttgart, Germany) to purify modified vaccinia
Capacity
Resolution Flow rate
MA
Packed bed
Monoliths
66
ankara (MVA) virus with the aim to reduce DNA levels to ≤ 10 ng per dose (as required
by the WHO 1998). A combination of two membrane adsorbers were used, one with
sulfated cellulose (SC) and the other with heparin ligands. A protein present on IMV
(A27L) is known to bind to heparin (Chiu et al. 2007) making this an attractive
candidate as a pseudo affinity ligand. It was found that both strategies worked; HIC
combined with SC-MA and Heparin-MA gave similar total virus particle yields of
approximately 60 %. Both were able to reduce HCP levels to lower limit of
quantification (LLOQ), and final DNA levels were comparable and as low as
10ng/dose.
It was concluded that nuclease treatment would still be required to robustly reduce DNA
to specified levels; however, as the amount of DNA was reduced significantly compared
to other technologies such as ultracentrifugation, the amount of nuclease treatment
could also be reduced, resulting in a more cost effective process.
SC-MA has also been utilised for MDCK cell culture derived influenza virus capture.
H1N1 and H3N2 were both purified as described by (Opitz, Zimmermann, Lehmann,
Genzel, Lubben, REICHL, & Wolff 2008). In this study, commercially available CIEX-
MA and column-based cellufine sulfate were compared to SC-MA. SC-MA showed
higher infectious virus recovery as well as increased DNA removal when compared to
both technologies. High flow rates (15 mL/min) coupled with a higher binding capacity
(13 µg HA/cm2) were achieved in comparison to column-based cellufine sulfate
chromatography, also resulting in an added process productivity benefit over this
technique.
It is clear from both studies that pseudo-affinity-based membrane adsorbers are an
attractive option for capture and polish of large enveloped viruses. It should be noted
67
that neither of these supports are available as catalogue items from Sartorius. But the
technology shows potential and clearly has advantages over packed bed
chromatography. There is still room for development to increase vaccinia virus yield
from 60 % and remove more DNA to achieve concentrations below 10 ng DNA/dose,
however; if GMP compliant columns are available then pseudo-affinity membrane
adsorbers might be an attractive process option.
2.4 Monoliths
2.4.1 History and Overview
There are now several types of monolithic materials on the market. Originally monoliths
were made from N,N’–methylenebisacrylamide; however, this was replaced with
piperazine diacrylamide and methacrylamide when it was found that these compounds
allowed for higher flow velocities (Svec 2008). Process scale commercial monoliths are
now made from poly(glycidylmethacrylate-co-ethyleneglycoldimethacrylate) or
Styrene-divinylbenzene (CIM® BIA separations, Ajdovscina, Slovenia), and currently
BIA Separations are the only company that can make up to 8000 mL columns (Ales
Strancar 2011).
Larger scale columns are currently in R&D phases, as manufacturing hurdles are
encountered during the polymerisation reaction due to the heat released across the
structure, potentially causing structural distortion if not kept within a certain range. The
polymerisation reaction which combines glycidyl methacrylate and ethylene
dimethacrylate in a free radical bulk polymerisation reaction with benzoyl peroxide as
an initiator has an activation energy Εa,app of 81.5 kJ/mol with a heat of reaction (∆Hr) of
190 J/g (Mihelic et al. 2001).
68
All monolith columns produced by BIA Separations from 1 mL up to 8000 mL are run
in radial rather than axial flow as this reduces their footprint whilst maintaining a
minimum bed length required to ensure a low pressure drop (∆P). Smaller commercially
available columns from 0.1 mL to 0.34 mL are run in axial flow as this is mechanically
easier to fabricate at small scale, owing to their smaller cross sectional area to bed
length ratio. As a result of this difference in area to bed length ratio, it is advised by the
manufacturer to perform scale up studies using 1 mL columns as a minimum.
Other manufacturers of monoliths are BioRad (Hercules, CA, USA) who have produced
polyacrylamide UNO columns. These are advertised for their high resolution at high
flow rates for protein separation. Merck have produced a silica-based monolith called
Chromolith, but have concentrated on the analytical ability of the columns taking
advantage of their high number of theoretical plates compared to packed bead resins of
an equivalent bed length. GE Healthcare has produced monobeads, which are made
from polyvinyl alcohol and polystyrene-coated. These can be packed into traditional
chromatography columns and are marketed for their high resolution and flow rate
capabilities.
2.4.2 Theoretical Considerations when using Monoliths
Purification of large macromolecules such as viruses, VLPs and pDNA, is typically
restricted by very low dynamic binding capacities (DBC) when using conventional
packed bed chromatography. For this reason chromatography has not conventionally
been the first method of choice for macromolecular separation.
Vaccinia virus, which is one of the largest known viruses, has an approximate diameter
of 300 nm. The pore opening on the surface of commercially available chromatography
69
media tends to be around 100 nm in diameter. Most viruses, therefore, can only bind to
the outer surface of these beads and so cannot utilise the full surface area available.
Packed bed columns are designed for the purification of relatively small molecules such
as therapeutic proteins, mAbs and peptides. These molecules can easily diffuse into the
micropores on the surface of typical bead-based resins, so the surface area available for
adsorption is very large and hence not a limiting factor. Manufacturers can further
optimise capacity as well as increase the number of theoretical plates of their resins by
reducing bead diameter and increasing ligand density. Efficiency is directly related to
particle diameter and is described by the number of theoretical plates or equilibrium
stages in a column per meter. The height of each theoretical plate (HETP) measured in
meters can be determined by the equation below,
𝐻𝐸𝑇𝑃 = 𝐴 + 𝐵
𝑢+ 𝐶. 𝑢 2-1
where A is eddy diffusion (m), B is longitudinal diffusion (m2
s-1
), C is the resistance to
mass transfer (s), and u is linear velocity (m s-1
).
This expression can also be expanded for process IEX chromatography as described in
(Jungbauer 2005;Lettner et al. 1995).
𝑯𝑬𝑻𝑷 = 𝟐𝜺𝒃 𝑫𝑳
𝒖+
𝟐𝒖
𝟏−𝜺𝒃 [
𝒌′
𝟏+𝒌′]
𝟐
[𝒓𝒑
𝟑𝒌𝒇+
𝒓𝒑𝟐
𝟏𝟓𝜺𝒑𝑫𝒑] 2-2
where 𝜀𝑏 is bed porosity, 𝜀𝑝 is particle porosity, 𝐷𝐿 is the axial dispersion coefficient
(m2
s-1
), 𝐷𝑝 is the particle diffusivity (m2
s-1
), 𝑘′ is relative retention factor, 𝑟𝑝 is particle
radius (m), and 𝑘𝑓 is the particle film mass transfer coefficient (m s-1
).
70
Due to mechanical constrains, limitations do exist regarding scale up of packed bed
columns (Jungbauer and Hahn 2008). High back pressures due to bed compression
towards the centre of a packed bed, as well as areas of non-linear flow distribution,
particularly at the column walls, can occur if the column diameter is increased above a
certain point. This is not thought to be a serious limiting factor for protein separations in
terms of productivity, due to the high binding capacities now achievable with
commercially available resins, 40-100 g/L in some cases. If packed bed columns were
to be used for viral separations, however, especially for high dose applications, column
size may become a bottleneck as capacity will be significantly lower, potentially by
orders of magnitude.
When considering the purification of viruses monoliths are becoming extremely useful
tools (Jungbauer & Hahn 2008). Monoliths have excellent mass transfer properties, as
reviewed and demonstrated by a number of authors including (Gritti et al. 2003;Hahn et
al. 2002;Jungbauer & Hahn 2008;Zou et al. 2002). However controversy exists as to
whether the observed mass transfer characteristics are due to the open pore structure and
subsequent convective flow or the small size of particles used in the polymerisation
process (Hahn, Panzer, Hansen, Mollerup, & Jungbauer 2002). The generation of an
open pore structure seen in CIM monoliths is due to the polymerisation process; small
particles ~1 µm in diameter agglomerate into a single, rigid polymeric homogeneous
structure called poly(glycidylmethacrylate-co-ethyleneglycoldimethacrylate). While the
overall surface area of monoliths is smaller than that of a conventional packed bed
chromatography column, the surface area available to large macromolecules for
adsorption is much higher. The three dimensional structure of monoliths has been
described as similar to membranes, which have in turn been described as very thin
monoliths (Ales Strancar 2011). They are highly porous and contain a vast amount of
cross-linking. This allows them to exhibit very low back pressures at high flow rates,
71
even at process scale. The HETP and dynamic binding capacity observed are almost
completely unaffected by velocity; therefore monoliths can in theory give very fast,
efficient separation of large macromolecules.
As Illustrated in Figure 2.5 the velocity profile in monolithic structures is important
when discussing their performance in comparison to packed bed chromatography and
especially membrane adsorbers.
Figure 2.5 An illustration to show the cross linking wide channel structure, and almost flat
velocity profile achievable using monoliths. The red arrows represent directional flow and
their size represents relative velocity. This picture was adapted from (Gagnon 2008).
A phenomenon known as eddy diffusion and axial dispersion occurs between the beads
of packed bed chromatography columns, causing band broadening and reduced
performance due to inter-particulate mixing. The velocity profile through a monolith on
the other hand is incredibly flat and relatively constant across the whole structure. This
72
eliminates eddy diffusion and axial dispersion, and hence, minimises band broadening
as there is no mixing occurring within the column. Mass transfer in monoliths, is
therefore not limited by diffusion like in a packed bed column.
NA = ka ∆ CA = ka (CAo – CAi) 2-3
where NA is the rate of mass transfer (kg s-1
), k is the mass transfer coefficient (m s-1
), a
is the area available for mass transfer (m2) ∆ CA is the change in concentration of
component A between the phase boundary and the interface as described by (CAo – CAi).
CAo is the concentration at the phase boundary of component A (kg m-3
) and CAi is the
concentration at the interphase of component A (kg m-3
).
In a packed bed column, which is limited by diffusion, this equation can be rewritten as
NA = kL a (CAo – CAi) 2-4
where kL is the liquid-phase mass transfer coefficient (m s-1
).
The main resistances to mass transfer in a packed bed column are shown in Figure 2.6
for reference.
73
i) Convective mass transfer from bulk liquid to boundary layer
ii) Diffusion across liquid boundary layer
iii) Diffusion into micropore
iv) Adsorption at solid liquid interface
v) Surface diffusion
Figure 2.6 An illustration showing the resistances to mass transfer involved in the
adsorption of a solute molecule to a ligand within a micropore of a resin bead in a
packed bed system adapted from (Bak et al. 2007).
74
A typical adsorption isotherm for a short bed monolith is highly favourable and often
fits with Langmuir isotherms, as shown in Equation 2-5.
q = 𝑞max 𝑐
𝐾𝐷+𝑐 2-5
where qmax is the maximum solute binding capacity (kg m-3
), KD is the dissociation
constant (m3 kg
-1), c is the concentration of solute in the mobile phase (kg m
-3), and q is
the concentration of solute bound to the adsorbent (kg m-3
).
The generation of adsorption isotherms is challenging when using infectious virus as a
starting material. This is due to the complex nature of the analytics. Viral quantification
assays tend to be incredibly variable with percentage coefficients of variance of >30%
routinely. This is a challenge when making any process development decisions, but
when attempting to create isotherms to describe adsorption equilibrium for modelling
applications a higher degree of certainty is required. Efforts to model mass transfer
properties of monoliths have therefore been performed using model proteins such as
BSA, lysosyme and IgG (Hahn, Panzer, Hansen, Mollerup, & Jungbauer 2002), which
can be quantified easily using UV absorption-based techniques.
As previously described, monolith porosity is very high (ε > 0.5). In practical terms this
makes it possible to operate monoliths at high flow rates without significant pressure
drops which, coupled with high binding capacities for large macromolecules, results in
significant advances in productivity compared to packed bed chromatography. CIM
monoliths from BIA Separations can be operated at >10 column volumes (CVs)/min at
small scale (1 mL) and ~ 1 CV/min at large scale (8000 mL). In comparison, the
majority of packed bed columns are run at between ~0.2 – 0.5 CV/min.
75
2.4.3 Purification of large complex macromolecular entities using CIM
monoliths
2.4.3.1 Introduction
Monoliths have been used for a large range of applications, such as the capture and
purification of viruses, VLPs, and bacteriophage, which are discussed in this section.
Besides this, literature from BIA Separations shows that CIM monoliths have also been
used for purifying transfection grade pDNA, IgG, IgM, His-tagged proteins, and His-
containing proteins (BIA separations 2010).
2.4.3.2 Virus purification using monoliths
In this section the following will be reviewed:
Vaccinia virus
Tomato mosaic virus
Potato virus Y
∆NS1-Influenza A + B virus
Baculovirus
Adeno-associated virus (AAV)
Lentivirus
Adenovirus type 3 dodecahedric virus-like particles
VLPs generated in Saccharomyces cerevisiae
Staphylococcus aureus bacteriophages
Bacteriophases, Lambda, T7, and M13
Following a review of the literature, there has been some recent work on MVA
purification using CIM monoliths, but authors have indicated severe challenges in
removing DNA to the required specifications of 10 ng/dose (Yang 2013). The authors
suspected a strong association between MVA and genomic DNA, which appeared to
prevent viral enrichment during IEX or HIC chromatography. What is interesting is
76
that this interaction appeared to be reversed after the addition of certain chaotropic
agents. 250 mM NaBr and 150mM KCl were added to CR.pIX avian cell culture
infected with MVA prior to lysis. Diatomaceous earth was then added post lysis and
incubated for 20 minutes at room temperature before being removed by filtration using
0.2 µm glass fibre disks. Subsequent IEX monolith chromatography was able to yield a
concentration of DNA/dose (assuming a relatively low dose of 1x108 plaque-forming
units (pfu)) of 2 fold above the acceptable levels for clinical applications (Jordan et al.
2015a). The percentage yield of infectious virus achieved is not available. This work
clearly outlines the challenges and complexity of removing contaminating DNA from
viral feed streams and indicates a clear unmet need to develop a process capable of
removing DNA and other impurities in order to develop vaccinia virus at the required
levels of purity in an economically feasible way. This will become particularly urgent if
clinical trials currently involving the development of vaccinia virus for oncolytic
applications are successful.
A study by (Kramberger et al. 2004) used CIM quaternary amine (QA) monolith disks
to concentrate tomato mosaic virus (ToMV) by several orders of magnitude. This
technique was intended to be, and hence optimised, for the concentration of ToMV in
irrigation waters, which are typically well below the limits of detection by standard
ELISA quantification assays. It was shown that even low concentrations of virus in
irrigation water can infect plants and cause losses to crops, particularly in a greenhouse
environment. The method development focused around viral concentration, with the
main optimisation criteria being percentage yield. This was so that the total amount of
virus eluted from the column could then be measured for a given volume of water so as
to determine the relative concentration of virus in the water system. The yield achieved
from CIM QA monoliths after two parallel experiments was 90% and 89% by ELISA,
respectively. Virus was eluted in 1.5 M NaCl in sodium acetate buffer pH 5.5.
77
ToMV was also purified using CIM QA monoliths as described by (Kramberger et al.
2007) for the development of a preliminary quantification technique to replace ELISA
and reverse transcriptase PCR (RT-PCR) technologies, which are labour intensive and
take a long time to complete. The study describes the generation of a standard curve by
loading five samples on the CIM QA monoliths at different concentrations of between
0.1 mg/mL and 0.43 mg/mL. The virus was eluted from the column using a linear
gradient between 0-1.5 M NaCl in sodium acetate buffer, and the corresponding peak
height was measured by UV absorbance at a wavelength of 280 nm. This data was then
used to generate a standard curve for the assessment of unknown virus concentrations
using the same method.
At preparative scale CIM QA was able to purify ToMV removing >99% of the
contaminating DNA and all quantifiable protein. The percentage viral recovery was
shown to be as high as 90%. Virus was elution between 0-1.5 M NaCl in sodium acetate
buffer using a multi-stepwise elution schedule. This is comparable to conventional
ultracentrifugation-based purification of ToMV; however, the authors point out that the
process can be shortened from 5 days to 2 hours when using monoliths.
More recently, CIM QA 0.34 mL monoliths disks were used to purify Potato virus Y
(PVY) produced from inoculated leaves from N tobacum cv White Burley and both
leaves and stems from Solanum tuberosum cv Pentland plants in a study by (Rupar et al.
2013). This was the first time that a filamentous virus had been purified using monoliths
and showed a number of purification challenges due to its size, shape and flexibility.
The dimensions of PVY are 700 nm x 11 nm. Impurities originating from the plant cell
lysate material such as cell wall components, pigments, poly-phenols and a variety of
host cell plant proteins and organelles posed challenges to purification due to their
variety and complexity. PVY is also known to aggregate, causing reduced yields during
78
purification. As with all infectious viruses, PVY presents an analytical challenge with
regards to the quantification of infectious virus particles and contaminants. To combat
this, the authors used a number of different analytical techniques in order to build a
complete picture of process performance. Transmission electron microscopy was used
to measure viral integrity, RT-PCR was used to measure PVY RNA, SDS-PAGE and a
260 nm/280 nm UV absorbance ratio was used to estimate viral purity. In the presence
of 0.5-1M urea, which was added to the load to prevent protein-induced aggregation,
99.9% of plant nucleic acids were removed as well as all quantifiable protein. Up to
50% of the viral RNA loaded was recovered across the elution factions, which after
inspection using TEM contained integral PVY. The infectivity of the recovered virus
was tested by infecting healthy N. tobacum plants with purified PVY, and it was found
that infectivity was not significantly affected by CIM monoliths. Elution was performed
in two parts, first a stepwise elution was run from 0.1 - 0.25 M NaCl and then a linear
gradient was run between 0.25–1 M NaCl over 100 column volumes with Tris buffer
containing EDTA.
Ion exchange CIM monoliths have been used to purify Vero cell derived ∆NS1 live
replication deficient influenza virus A and B (Banjac, Roethl, Gelhart, Kramberger,
Jarc, Jarc, Strancar, Muster, & Peterka 2014). ∆NS1-H3N2, ∆NS1-H1N1, ∆NS1-H5N1,
and ∆NS1-Influenza B were all tested. ∆NS1-Influenza A virus strains were found to
adsorb to both strong and weak AIEX monoliths, DEAE and QA respectively, as well
as strong CIEX CIMSO3 monoliths all at neutral pH. Interestingly, this was not the case
for ∆NS1 Influenza B virus which did not adsorb to CIMSO3. The dynamic binding
capacity measured for ∆NS1-H1N1 was 1.9x1010
pfu/mL on CIM QA monoliths,
1x1010
pfu/mL on CIM DEAE, and 8.9x108 pfu/mL on CIMSO3. After scale up to 8
mL CIMQA monoliths, process reproducibility was tested on all viruses. Average
infectious virus recovery was between 70.8% ± 32.3% and 87.9% ± 30.8%. Percentage
79
removal of contaminating protein was between 93.3% ± 0.4% and 98.6% ± 0.2% and of
DNA between 76.4% and 99.9%. All viruses were eluted in HEPES buffer in 500 mM
NaCl pH 7.5.
A study by (Gerster et al. 2013b) mentions the use of Benzonase (Merck KgA,
Darmstadt Germany) to reduce DNA load challenge going onto CIM monoliths. The
study deals with the development of CIM QA monoliths to purify infectious baculovirus
produced by Spodoptera frugiperda cells (Sf-9) using a stepwise elution schedule. It
also illustrates the potential use of epoxy monoliths for the removal of lipids from the
feed stream run in flow through mode. The percentage recovery of infectious virus was
shown to be highly dependent on the age of the load and its composition; DNA
concentration, levels of aggregation, and ratio of infectious to inactivated virus. The
recovery of infectious virus in the main elution pool was between 20% and >99%. The
total protein removal was reported to be between 92% and 99%, and DNA removal
between 52% and 62%. Virus was initially eluted over a linear NaCl gradient between
0.2–1 M, however, this was later redesigned and virus was eluted using a multi-stepwise
elution schedule. This paper stresses the difficulty to report a full picture due to the
complex nature of the system, but does use a range of orthogonal assays in an attempt to
generate process understanding. These studies illustrate the reduced time needed to
purify large amounts of virus using monoliths compared to ultracentrifugation, and the
authors suggested that further scalability studies would be worthwhile in order to
develop a viable large scale process.
A range of recombinant Adeno-Associated Virus serotypes (rAAV) have been
successfully purified using CIM QA monoliths. AAV1 capture using CIM QA
monoliths has been shown to yield between 40% and 72% prior to affinity-based AVB-
Sepharose (GE Healthcare, Uppsala, Sweden) as a second step (Wang et al. 2009).
80
Virus was eluted using a stepwise elution schedule in Tris buffer containing 260 mM
NaCl. In a separate article, separation of full and empty AAV8 capsids using CIM QA
monoliths was performed on 0.34 mL disks (Lock et al. 2012). Virus was eluted over
shallow gradients from 80-115 mM NaCl over 20 column volumes. Two peaks were
reported over the elution, the first containing empty capsids, these are vectors lacking a
genome, and the second containing up to 99% full, genome containing capsids. Results
were confirmed both by negatively staining TEM, which showed empty capsids with an
electron dense core, and qPCR. This was originally developed as an analytical
technique, however, BIA Separations now market CIM QA as both an analytical tool to
estimate full/empty ratios and as a preparative method to remove empty capsids from
purified AAV material.
CIM DEAE weak AIEX monoliths have been used to purify HIV-1-derived lentiviral
vectors (LVs) (Bandeira et al. 2012). LV is known to be very unstable. This is likely
due to the degradation of its envelope surrounding the capsid causing loss of infectivity.
This often occurs over typical processing conditions during both ultracentrifugation and
chromatography using membrane adsorbers, which have been shown to recover ~30%
of infectious LV. CIM DEAE achieved a recovery of up to 80% and removed up to
88.8% of contaminating DNA prior to the addition of Benzonase. After Benzonase
treatment the whole process was shown to remove 99.9% of contaminating DNA, with
an overall infectious LV recovery of 36%. This included losses over depth filtration,
chromatography using CIM DEAE, ultrafiltration, and size exclusion chromatography
as a final polishing step. LV was eluted in Tris buffer using a stepwise elution schedule
at 0.65 M NaCl. A pre-elution wash step contained 0.1 M NaCl was also employed to
remove contaminating protein.
81
In the examples shown above, the main recurring challenge has been to remove
genomic DNA from viral feed streams. The main reason why DNA is a problem is that
many viral processes require cell lysis. This means that high concentrations of cellular
DNA are present in the harvest prior to purification. The exact amount will depend on
the cell density and type of primary recovery steps being used. CIM monoliths have
shown the ability to efficiently adsorb infectious virus material. Over 50% of virus
material loaded is commonly eluted without significant loss in infectivity, and DNA and
protein can be removed to an acceptable level with the addition Benzonase. One of the
main attributes that will likely drive the industry away from ultracentrifugation is
productivity, as many authors have reported significant gains in terms of time when
using monoliths with comparable or improved purity and yield. There is clearly more
work to be done to improve process performance with monoliths; however, as often
shown in the literature, it is currently difficult to draw conclusions due to analytical
challenges.
2.4.3.3 Virus like Particle VLP purification using monoliths
CIM monoliths have been successfully used to purify a number of VLPs from partially
purified and crude cell lysate. Work by (Urbas et al. 2011) has shown that CIMac QA
columns, which are 0.1 mL analytical monoliths supplied by BIA Separations, are able
to purify recombinant adenovirus type 3 dodecahedric virus-like particles (Ad3 VLP)
expressed in a baculovirus / insect Sf21 cell systems. The recovery of biologically
active VLP achieved by CIMac QA monoliths was shown to be 52%, with levels of host
cell DNA removal greater than 99%. The monolith process also allowed the purification
to be reduced from 5 days, when using ultracentrifugation, to 1 day when using
82
monoliths. VLPs were eluted between 0-1 M NaCl initially using a linear gradient, and
then latter using a multi-stepwise elution schedule in Tris buffer containing EDTA and
glycerol. While CIMac columns are primarily used for quantification, this work
demonstrated that they can also be used to screen and optimise preparative purification
processes.
Work done as part of an EngD collaboration between University College London and
BIA Separations as described by (Burden et al. 2012) uses CIM OH monolith to purify
VLPs produced in Saccharomyces cerevisiae. This study is another good illustration of
the potential of monoliths to purify large macromolecules as an alternative to either
ultracentrifugation or conventional packed bed chromatography. The author describes
problems associated with lipid contamination in the feed stream and outlines a
methodology for the removal of lipids prior to the purification process, which was
shown to double the DBC and increase the life time of the monolith column. Amberlite
/ XAD-4 beads were used to remove 70% of the lipids, whilst recovering 80% of the
VLP. This increased the CIM OH DBC from 0.11 mg/mL to 0.25 mg/mL. In
comparison to Butyl-S Sepharose 6 FF, CIM OH monoliths achieved a DBC of
approximately 3-4 fold higher. The recovery of VLP was comparable, 85% from CIM
OH and 90% from Butyl-S Sepharose 6 FF. CIM OH load buffer contained 1 M
Ammonium sulfate, while Butyl-S Sepharose 6 FF only required 0.6 M. Elution was
achieved by a stepwise schedule in phosphate buffer from both columns without
ammonium sulfate.
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2.4.3.4 Bacteriophage purification using monoliths
A study by (Kramberger et al. 2010) focuses on the purification of Staphylococcus
aureus bacteriophage (phages) VDX-10 using CIM QA monoliths. This was developed
as a one-step purification process, and was scaled up from 0.34 mL CIM QA disks to an
8 mL column. Viable phage recoveries were recorded between 54% and 65%, DNA
removal was between 99.4% and 99.8%, and total protein removal was between 90%
and 91%. Phage was initially eluted in a linear gradient between 0-1M NaCl over 100
columns volumes. When the process was scaled up, a step wise elution schedule was
used at 0.6 M NaCl.
In a separate study (Smrekar et al. 2011) three different phages, T7, lambda and M13
were purified using CIM QA monolith disks. The binding capacity of M13 was found to
be very high, 4.5x1013
pfu/mL. T7 and Lambda phages both showed lower capacities
closer to 1x1013
pfu/mL. All three phages were eluted in a linear gradient between 0-1M
NaCl over 20 column volumes with 100% recovery of all phages recorded. Lamda
phage, however, required the addition of 0.2 M NaCl in the load in order to reach 100%
recovery and maximum DBC.
In conclusion, commercially available CIM monoliths are able to remove impressive
amounts of host cell DNA and protein from viral feed streams without compromising
viral infectivity. Monoliths offer significant advantages in terms of productivity over
traditional unit operations like ultracentrifugation, although differences in infectious
virus recovery and purity between the two technologies depend on the virus in question.
For example, the percentage recovery of Baculovirus has been reported to be less than
1% following ultracentrifugation (Chen et al. 2009), while human respiratory syncytial
virus can reach infectious recoveries of 69% according to (Gias et al. 2008).
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The next section will focus on viral analytics, which is a massive challenge and
bottleneck in process development. For chromatography to become indispensable for
the purification of virus material, conclusions drawn during process development
regarding process performance need to be accurate. This is currently not possible in
many cases as viral analytics are too variable, and no one assay is able to tell the whole
story.
2.5 Virus Bioprocess Analytics
2.5.1 High Level Analytical challenges faced during virus bioprocessing
There are a number of reasons why viral analytics can be a challenge. Prior to the
commencement of process development, a precise understanding of exactly what the
starting material is and what impurities are present is crucial. The answer to these
questions, however, is complex. Many assays are needed in order to generate a realistic
approximation of viral titre as questions relating to the number of infectious viral
particles, number of transducing units, number of whole, integral viral particles, number
of empty viral particles, number of full viral particles, and number of full particles
containing the correct genetic material all need to be answered depending on the virus in
question. All of these assays add layers of complexity and all of this information needs
to be put together in order to generate robust conclusions.
To complicate this further, viruses, unlike most proteins, are generally expressed
intracellularly. This means that cells need to be lysed in order to release the virus into
the supernatant. Some viruses are lytic, meaning they can be left to lyse the cells
themselves, others require chemical or mechanical treatment in order to lyse the cells.
During this process, cellular components such as host cell DNA, proteins, and lipids, if
85
using mammalian, insect or avian expression systems, or cell wall components,
pigments, and poly-phenols if using plant cells (Rupar, Ravnikar, Tusek-Znidaric,
Kramberger, Glais, & Gutierrez-Aguirre 2013), are released into the supernatant along
with the virus. Assays need to be developed that are able to quantify all of these
contaminants, especially if they are considered to be critical quality attributes (CQAs).
One of the major contaminants in viral therapies is dsDNA. According to the WHO,
Technical report series, No 926, 2004, (World Health Organisation 2004) a smallpox
vaccine needs to have less than 10 ng of cellular DNA per dose.
The Host Cell Protein concentration in a final drug substance is often defined as a CQA,
however, typical specifications for viral therapies in terms of mass of HCP per viral
dose is not as specifically defined as it is for dsDNA.
The reason for this is that acceptability criteria for HCPs will often depend on the
production cell line used, specific HCP populations known to be present, and any
clinical safety data available. The regulators typically suggest an open dialog with
manufactures and require evidence of HCP removal across the process using sensitive
and qualified assays. A justification for in-house acceptability criteria is then put
forward by the manufacturer.
According to International Conference on Harmonization (ICH) guidelines Q6B: “For
host-cell proteins, a sensitive assay (e.g., immunoassay, capable of detecting a wide
range of protein impurities) is generally utilized. In the case of an immunoassay, a
polyclonal antibody (pAb) used in the test is generated by immunization with a
preparation of a production cell minus the product-coding gene, fusion partners, or other
appropriate cell lines.(…) “Clearance studies, which could include spiking experiments
at the laboratory scale, to demonstrate the removal of cell substrate-derived impurities
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such as nucleic acids and host cell proteins may sometimes be used to eliminate the
need for establishing acceptance criteria for these impurities”.
The FDA and EMA have similar wording each requiring the use of sensitive and GMP
qualified assays to monitor HCPs according to ICH guidelines. According to (F.Wang
et al. 2015) the actual speciation of HCPs is determined on a case by case basis but is
often between 1 and 100 ng/mg of protein. It should be noted that this range in
specification is likely taken from the biopharma sector, and therefore relevant for
products such as a mAbs and therapeutic protein rather than ATMPs.
1-100 ng/mg does not translate well to viral vectors as 1 mg of vaccinia would be
approximately 5x1012
viral particles; this is assuming the mass of 1 vaccinia virus
particle is 5 fg. A dose of vaccinia virus is likely to be around 1x109 infectious particles
per injection so if the specifications were set to 100 ng/mg of virus then the total mass
of HCPs per vaccinia dose would need to be 0.02 ng. This is unlikely to be achievable.
For this study total protein has been recorded across each unit operation and shown to
be reduced to LLOQ levels based on the BCA assay. An immunoassay such as an
ELISA would be more appropriate for a GMP compliant process to show protein
removal, however, it was decided that data from the BCA assay would be good enough
to generate process knowledge and understanding while working with vaccinia at UCL.
The main reason for this was cost and availability.
When conducting process development of viral vectors for gene therapy applications in
industry both PicoGreen and BCA assays are routinely used to test in process samples
to show removal of dsDNA and total protein. This is particularly true during early phase
development. More specific ELISA assays are then developed, qualified and used for
product release testing and as required for in process control following dialog with the
regulators.
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2.5.2 Current analytical methods used in virus bioprocessing
It is relatively straightforward to get a measure of the total number of particles,
assuming equipment availability, in a pure viral sample. Harvest samples are very
difficult to analyse without specific assays such as ELISAs as the majority of particles
are not viral. Dynamic light scattering (DLS) using a Zetasizer, and Nanoparticle
Tracking Analysis (NTA) using a NanoSight, both supplied by Malvern Instruments
(Malvern Instruments, Worcestershire, UK), can be used to generate particle size
distribution data. DLS is not able to reliably estimate the concentration of particles
especially in polydisperse samples due to bias caused by large particle in the solution.
NTA does not suffer from this issue as the instrument measures individual particles
separately. Neither of these techniques are specific to viral particles. Both the DLS and
NanoSight measure the movement of particles and calculate size based on Brownian
motion.
NTA works as follows:
A laser is passed into a chamber containing the sample in suspension at between 1x106
and 1x109 particles/mL. The particles scatter the laser beam which is detected by a
video camera mounted on top of a microscope permanently set at 20x magnification.
The camera records frames of the particles moving under Brownian motion at lengths of
time determined by the operator. The particles are then tracked and the hydrodynamic
diameter is calculated based on specified temperature and viscosity inputs according to
the Stokes Einstein equation shown below.
𝑑𝐻 =𝑘𝑇
3𝜋𝜇𝐷 2-6
where dH is the hydrodynamic radius of a particle (m), k is the Boltzman constant (J/K),
T is temp (K), µ is viscosity (Pa.s) and D is the diffusion coefficient (m2s
-1).
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Tunable Resistive Pulse Sensing (TRPS) equipment supplied from IZON (IZON
Oxford, UK) is an alternative system that uses impedance measurements rather than
Brownian motion to estimate particle size. A voltage is placed across a tuneable
nanopore at constant pressure, and a measurement is made by recording the resistive
pulse generated as a particle traverses through. As particles traverse they block the flow
of ions through the nanopore and the resulting voltage impedance is measured. The
magnitude of the resistance pulse in proportional to the particle size and velocity. TRPS
can also measure the zeta potential of individual particles in a sample. This is calculated
from the electrophoretic mobility according to the Smoluchowski equation. As the
magnitude of the resistive peak is independent of the zeta potential the size and zeta
potential can be measured simultaneously for each particle traversing the nanopore.
Flow cytometry, specifically the Virus Counter 3100 from Virocyt (Virocyt CO USA) is
a fourth option, this system using general nucleic acid and protein stains to tag viral
particles containing both DNA and protein. Particles are then passed through a laser
probe region one by one and those emitting florescence from both stains are counted as
integral viral particles.
All four systems described above will approximate the total particle concentration in a
sample, but each has a very low specificity. What this means in practice is that all four
assays will overestimate total virus concentration in a sample, even if only taking into
account particles within the specific size range corresponding to the virus. This will be
especially true when analysing impure samples.
In order to quantify total particle concentrations more specifically, ELISAs are
commonly used. ELISAs use antibodies to bind to conformational epitopes on viral
surfaces. Conjugated antibodies are then used to generate a colour change that can be
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detected by absorbance plate readers. A common example (AAV2 Titration ELISA,
Progen Biotechnik GmbH, Heidelberg, Germany) uses a biotin-conjugated antibody to
generate a colour change when streptavidin and a substrate are added.
In order to quantify infectious titre, infectivity assays need to be performed. Examples
are Tissue culture infectious dose (TCID50) assays and plaque assays. Transduction
assays can also be used, although only for virus that can transduce cells in culture.
These assays are cell-based and require trained personal to perform, often taking 5-6
days to obtain a reading. One of the most challenging aspects, however, is the
variability commonly seen between replicates. This is routinely above 30% (RSD), but
can be as high as an order of magnitude.
The use of a large variety of orthogonal analytical methods to help build a quantitative
picture of the system in question is currently thought to be the best option when
analysing virus material. This is generally accepted as best practice amongst industry
professionals and academics within the field. As previously mentioned, there is no one
assay that can provide all the information.
The following table, while not exhaustive, gives a detailed overview of some of the
more commonly used techniques for analysing virus material.
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Table 2.4 Commonly used analytical methods for the quantification of viral CQAs
Assay Application Advantages Limitations
Quantification of total viral particles and related impurities
Analytical
Chromatography
- Total viral particle
quantification
- Fast
- Sensitive
- Fairly high precision
- Requires base line resolution
- Relatively low specificity unless affinity-based
- Indirect measure of viral particles / based on
standard curve
ELISA (direct or
sandwich)
- Total viral particle
quantification
- Sensitive
- Specific
- Fairly high precision
- Indirect measure of viral particles / based on
standard curve
- Fairly time consuming
- Expensive
Electron
Microscopy
- Visualisation of
virus integrity
morphology and
polydispersity
- Very high resolution
- Generates a visual image
- Expensive
- Requires significant expertise
- Prone to artefacts
Dynamic light
scattering (DLS)
- Particle size
distribution /
aggregation profile
- Quantification of
total particles
- Very Fast
- Sensitive
- Low resolution
- Struggles with polydisperse samples
- Non-specific
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Assay Application Advantages Limitations
NanoSight
(NTA)
- Particles size
distribution /
aggregation profile
- Quantification of
total particles
- Very Fast
- Relatively high resolution
- Generates a visual image
- Non-specific
qPCR
ddPCR
RT-PCR
- Quantification of
total viral genomes
- Fast
- Specific
- Sensitive
- (ddPCR generates absolute
quantification)
- Fairly high precision
- Indirect measure of viral particles
- Requires complex pre-treatment of sample to
remove residual viral DNA
Viral counter
InDevR
- Quantification total
particles (some
comparisons made
to infectious titre)
- Fast
- May give infectivity data
- Expensive equipment
- Low specificity
Quantification of infectious viral particles
Infectivity assays
(eg TCID50
Plaque Assays
- Quantification of
infectious viral
particles
- Specific (Infectious virus)
- Time consuming
- High variability
- Laborious
- Requires experienced operators
Cell-based
reporter gene
assays
- Quantification of
infectious
replication
- Specific (Infectious replication
competent/ transducing units)
- Time consuming
- High variability
- Requires experienced operators
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Assay Application Advantages Limitations
/transduction
assays
competent virus
Or
- Quantification of
transducing units
Quantification of process related impurities
Protein
Absorbance
Assays
BCA (or similar)
- Quantification of
total protein
- Fast
- Sensitive
- Fairly high precision
- Non-specific
- Relatively high LLOQ
RP HPLC
- Quantification of
lipids
- Sensitive
- Fast
- Can give high resolution
especially if coupled with mass
spec (LC/MS)
- Fairly high precision
- Requires base line resolution
- Relatively low specificity
DNA Florescence
Assays
(PicoGreen)
- Quantification of
total dsDNA
(ssDNA also
available)
- Sensitive
- Fast
- Fairly high precision
- Non-specific
- Reagent may be able to detect encapsulated viral
DNA
HCP (ELISA)
- Quantification of
host cell protein
(HCP)
- Sensitive
- Specific
- Fairly high precision
- Indirect measure
- Fairly time consuming
- Expensive
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2.5.3 Quantification of viral infectivity
Common assays utilised for the quantification of infectious virus particles are often
slow, laborious and prone to high variability and human error. The most common is the
plaque assay redeveloped from an early method designed to quantify bacteriophage in
1952 by Renato Dulbecco (Flint et al. 2004). The plaque assay works by incubating
adhering cells with a virus preparation and allowing it to be absorbed by the cells. The
inoculum is then replaced with a gel that prevents viral infection of other cells once the
infected cells on the plate are lysed. Circular zones are then formed around infected
cells called plaques, which are assumed to be the result of infection from one virus
particle. There are a number of variations of the plaque assay including the
Fluorescent-Focus assay and the Infectious Centre Assay.
The Fluorescent-Focus assay is very similar to the plaque assay but rather than adding a
gel, such as agar, cells are permeabilised with a solvent, such as acetone, and incubated
with an antibody against a specific viral protein. A second antibody with an attached
fluorescent marker is then added, which recognises the first. Infected cells can then be
examined using a fluorescence microscope. This technique is often used for viruses that
are not able to kill and lyse cells. The Infectious Centre assay on the other hand is
utilised to determine the number of cells in a culture that are infected with virus. It
works by growing the cells and allowing viruses to propagate for a short period of time
before taking an aliquot of the cell suspension and re-infecting known dilutions of the
harvested cells onto fresh adhering cells which are covered in a gel in the same way as
when performing a plaque assay. The number of resulting plaques formed is then a
measure of how many cells in the original sample were infected with the virus.
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The TCID50 assay, derived from a statistical method for deriving 50% end points by
(Reed and Muench 1938), works in a similar way to the plaque assay, however, does
not require the addition of a gel to quantify infection. Instead it relies on the
identification of cytopathic effect (CPE) when the cells are observed under a
microscope after the infection has taken place. Serial dilutions of the initial virus sample
are performed in a 96 well plate containing a monolayer of adhering cells in each well.
Plates are then incubated for 5-6 days, depending on the virus, at 37 ⁰C and 5% CO2.
There has been a lot of work in recent years to develop fast and effective reporter gene
assays for the quantification of infectious virus particles. The application of such assays
can be within a clinical setting, for example used for diagnostic testing, or can be a
process analytical technology (PAT) for virus bioprocessing. There are a number of
examples in the literature of reporter gene assays being used for virus replication
detection. A study by (Lutz et al. 2005) shows the use of both green fluorescence
protein (GFP) and firefly luciferase reporter genes to detect replication of Influenza
virus A in 293T cells. RNA polymerase I promoter/terminator cassettes expressing
RNA transcripts of either GFP or luciferase flanked by untranslated regions of the
influenza A were constructed and transfected into 293T cells. It was shown that after
infection with Influenza A the cells started expressing the reporter gene within 6-24
hours. The study used two different methods to detect expression of each protein, GFP
was detected using a dual laser excitation FACSCalibur flow cytometer (Becton
Dickinson, San Jose, CA, USA), whilst luciferase activity was measured in 293T cell
lysate using the Luciferase Assay System (Promega, Madison, WI, USA).
Reporter genes have typically been cloned into the backbone of viruses themselves
rather than into cell lines used for titre estimation. An example of how this has been
done is given in (Cosma et al. 2004). While this approach has been shown to work,
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there are some associated safety concerns. Viruses are known to drop large genes, if a
replicating virus was to drop a reporter gene in a patient cells it is unknown whether this
might be incorporated in the cellular DNA, and what the effects might be. This risk is
that residual DNA might contain oncogenes or infectious agents (Yang 2013).
To get around this issue there are a number of examples of virus induced cell-based
reported gene assays where genes are expressed in cell lines under the control of viral
promoters that express the gene of interest during infection. A study by (Levy et al.
2010) has used a strong poxvirus promoter 7.5-kDa-STR upstream of luciferase cloned
into a pGL4.10 vector and transfected into HeLa cells. The study also used green
fluorescence protein (GFP) for live cells assays. When virus infects the cell the
promoter sequence is activated by viral transcription factors that are able to pass though
the nuclear membrane. Infection causes translation and expression of the reporter gene
by the cell.
2.6 Summary
It is clear that vaccinia and other oncolytic viruses hold immense potential to enhance
the lives of global populations. Many companies are working with vaccinia and other
oncolytic viral vectors to achieve desirable patient outcomes, which could significantly
enhance survival rates for patients with difficult and hard to reach tumours. If this
potential is to be brought forward to a commercial setting, however, then affordable,
safe and well validated manufacturing regimes need to be established.
Vaccinia virus purification remains a challenge. While some work on vaccinia and other
large infectious virus particles has shown that monoliths and membrane adsorbers are
able to outperform unit operations such as ultracentrifugation in terms of productivity,
work to remove DNA and other contaminants whilst demonstrating a robust recovery of
96
infectious particles is still limited and needs to be understood and developed further.
Companies have already made significant investments in ultracentrifugation.
Ultracentrifugation is a tried and tested technique requiring very little process
optimisation. Recovery and purity of infections virus generated from most
ultracentrifugation processes is reasonably high and is acceptable for clinical
applications. As a result of this, the industry is cautious not to completely move away
from ultracentrifugation. Chromatography does have some significant advantages.
There are significantly more factors that can be optimised in order to generate superior
process performance. This makes it likely that chromatography will be able to
outperform ultracentrifugation. The problem is that as a result of the large number of
factors available for optimisation, chromatography requires a lot of process
development time, resources and expertise. This is made even more problematical due
to analytical challenges associated with estimating total and infectious virus, as robust
conclusions on process performance are difficult to generate. A large number of
orthogonal assays are needed in order to build up a picture of how a viral process is
performing.
97
Chapter 3
Materials and Methods
3.1 Introduction
All experimental methods undertaken during this work have been explained in a single
Materials and Methods section. This chapter is not intended to act as a list of standard
operating procedures (SOPs), but has been written in order to allow a reader to be able
to repeat any experimental procedure described in this thesis with appropriate training
and access to relevant manufacturer’s instructions.
3.2 Preparation of vaccinia virus load material
3.2.1 Viral production
Vaccinia virus was produced in Cercopithecus aethiops Kidney (CV-1) cells (ATCC)
and cultured in Corning® 175cm2 cell culture flasks (Corning, NY, USA) in an
incubator at 37⁰C, 5% CO2. Flasks were seeded at a cell density of 1x106
cells/flask,
measured by a haemocytometer. (All cell counts were measured in duplicate).
The media used was Dulbecco’s Modified Eagle’s Medium (DMEM), high glucose
(4500 mg/500 mL) containing Sodium pyruvate (110 mg/500 mL), and 4 mM L-
Glutamine. 5% Foetal bovine serum (FBS) was sterile filtered through a 0.22 µm sterile
Millex® syringe filter (Merck Millipore, MA, USA) and added to the media along with
1% penicillin/streptomycin purchased from (Sigma-Aldrich St Louis, MO).
98
25 mL of DMEM was used as a standard volume for all T175 flasks used.
Cells were infected with vaccinia once they were ~80% confluent. Confluence was
measured by eye; a single T175 flask was then trypsinised and the total number of cells
counting using a haemocytometer. The resulting count was used to determine the total
number of viral particles required per batch.
The multiplicity of infection (MOI) was set to 0.01 pfu/cell. The total number of cells
present per batch before infection was calculated as the total number of cells in a single
T175 flask multiplied by all 30 flasks per batch.
3.2.2 Primary recovery
Cells were harvested 72 hours post infection using a Corning Cell Scraper purchased
from (Sigma-Aldrich St Louis, MO). The resulting cell suspension was centrifuged at
1500 rpm for 5 minutes to pellet the cells and remove the supernatant. The supernatant
contains viral forms with an extra envelope derived from the cell membrane. Pelleted
cells were re-suspended in DMEM media containing 5% FBS and freeze thawed three
times to lyse cells and release virus.
Material was re-centrifuged at 2000 rpm for 5 minutes to remove cell lysate, collected,
and stored at -80⁰C until further processing.
It should be noted that the resulting material was freeze thawed 4 times, aliquots of 1
mL were taken at final thaw in order not to continually freeze and thaw the bulk
material prior to chromatographic or filtration experiments.
99
The size of a single batch of material was kept constant at 30x T175 flasks and yielded
~3x1010
infectious virus particles. The final concentration of material, herein referred to
as “harvest material” was 1.57x109 pfu/mL on average.
3.2.3 Preparation of chromatography load material
Harvest material was diluted 1:5 with equilibration buffer in order to achieve
appropriate salt and pH conditions for adsorption. Diluted material was then passed
through a 0.8 µm cellulose acetate syringe Minisart filter (Sartorius Stedim Biotech,
Sartorius AG, Goettingen, Germany) before being sampled and loaded onto the column.
The 0.8µm filter was put in place to protect the column from potentially fouling
materials such as large debris or viral aggregates. A process flow diagram is shown in
Figure 3.1.
100
Figure 3.1 Process flow diagram showing method used to produce vaccinia virus
throughout this work.
101
3.2.4 Chromatographic systems used
A number of different chromatography systems were used during this work. Initial
studies were performed using an AKTA Explorer (GE Healthcare, Uppsala Sweden).
Due to resource limitations, all CIM SO3 stepwise elution optimisation runs were
performed using a Pharmacia P-6000 FPLC pump connected to a Pharmacia
conductivity monitor. As a UV monitor was not available, elution fractions were
collected in fixed volumes and analysed for infectious virus, DNA, and total protein
concentration offline. An image of this setup is shown in Figure 3.2.
Figure 3.2 Chromatography system setup used for CIM SO3 stepwise elution optimisation
runs.
P-6000 Syringe pump
Conductivity meter
CIM SO3 monolith
102
CIM OH optimisation experiments were performed using an AKTA Pure (GE
Healthcare, Uppsala, Sweden) with all fractions collected using a F9-R fraction
collector. All monolith columns were run at 5 column volumes (CV)/min (195 cm/h) to
ensure scalability and all fractions were collected and stored at -80 ⁰C prior to
processing.
As vaccinia virus requires BSL II containment all chromatography experiments were
performed in dedicated CAT II laboratories in closed systems inside a biosafety level II
cabinet.
The chromatography method used for running each monolith chemistry is shown in
Figure 3.3.
103
Figure 3.3 Chromatography run setup used for CIM monolith stepwise elution runs
Removal of 20% ethanol storage solution
10 CVs purified water (2 mL/min)
CIP
10 CVs 1 M NaOH 2M NaCl (2 mL/min) 60 min
Regeneration step
20 CVs Elution buffer (5 mL/min)
Equilibration step
20 CVs Equilibration buffer (5 mL/min)
Product Load
Wash / Re-equilibration step
10 CVs Equilibration buffer (5 mL/min)
Elution
10 CVs Elution buffer (5 mL/min)
Strip
10 CVs Strip buffer (5 mL/min)
CIP
10 CVs 1 M NaOH 2M NaCl (2 mL/min) 60 min
Removal of CIP buffer
10 CVs Equilibration buffer followed by purified water (2 mL/min)
Storage
3 CVs 20 % Ethanol (2mL/min)
104
3.2.5 Monolith chemistries used
Four different commercially available Convective Interaction MediaTM
(CIM) 1 mL
monoliths (BIA Separations GmbH, Villach, Austria) were evaluated as potential
capture steps for vaccinia virus, each with a nominal channel diameter of ~6 µm.
The chemistries evaluated are listed below;
CIM SO3-1mL columns, strong cation exchangers due the presence of
sulfate groups, negatively charged between pH 2-13
CIM QA-1mL columns, strong anion exchanges, positively charged between
pH 2-13 due the presence of quaternary amine groups
CIM DEAE-1mL columns, weak anion exchangers due the presence of
diethylamine groups positively charged between pH 3-9
CIM OH-1mL columns, weak hydrophobic columns due to the presence of
hydroxyl groups. These are generated by the hydrolysis of epoxy groups.
Hydroxyl groups are negatively charged and are very hydrophilic
Monoliths were equilibrated with >20 CV of elution buffer, followed by >20 CV of
equilibration buffer after a 60 min sanitisation in 1 M NaOH 2 M NaCl. Wash and
elution fractions were both collected in 5 CV fractions throughout. All buffers were
sterile filtered prior to use using 0.25 µm membrane filters run in dead end mode at
constant pressure.
105
3.3 Buffer preparation
All buffers where purchased from Sigma – Aldrich unless otherwise stated.
Sodium phosphate monobasic monohydrate (NaH2PO4.H2O) was purchased as a
powder and has a molecular mass of 137.99 g/mol.
HEPES (4-(2-Hydroxyethyl)piperazine-1-ethanesulfonic acid) was purchased in powder
form and has a molecular mass of 238.3 g/mol.
The titrant used to pH HEPES and NaH2PO4 buffers was NaOH. This was also
purchased as a solid and has a molecular mass of 40 g/mol.
Tris base (2-Amino-2-(hydroxymethyl)-1,3-propanediol) was purchased as a powder
and has a molecular mass of 121.14 g/mol. Titrant used was HCL ACS reagent, 37%
purchased in liquid form with a molecular mass of 36.46 g/mol.
NaCl was purchased as a powder and has a molecular mass of 58.44 g/mol.
Benzonase® was purchased from Merk & Co Kenilworth, NJ, USA
Buffer used to dilute samples containing ammonium sulfate was:
10 mM Tris 2 mM magnesium chloride pH 8.
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3.4 Analytics
3.4.1 Quantification of infectious titre
TCID50 was the method of choice for the quantification of infectious vaccinia virus
particles, and was calculated using the Reed and Muench method (reed and Muench
1938) and expressed in pfu/mL.
The logarithm of the TCID50 was calculated as follows:
log 𝑇𝐶𝐼𝐷50 = 𝐿𝑜𝑔 (𝑑𝑖𝑙𝑢𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 𝑎𝑏𝑜𝑢𝑡 50% − (% 𝑖𝑛𝑓𝑒𝑐𝑡𝑒𝑑 𝑐𝑒𝑙𝑙𝑠 𝑎𝑏𝑜𝑣𝑒 50%
% 𝑖𝑛𝑓𝑒𝑐𝑡𝑒𝑑 𝑐𝑒𝑙𝑙𝑠 𝑏𝑒𝑙𝑜𝑤 50%))
(3-1)
This expression is a function of the dilution factor used in each row, and the percentage
of infected wells at that dilution factor. The TCID50 can be directly found from this
expression and this is then multiplied by the volume of diluted virus material added per
well in a 96-well plate format in order to generate a TCID50/mL value.
In order to then estimate the pfu/mL in a sample of a known TCID50, the Poisson
distribution is used as follows:
If we take the expression
𝑃(0) = 𝑒(−𝑚) (3-2)
where p(0) is the proportion of uninfected wells, and m is the mean number of
infectious units/volume. If we assume that the conditions for virus infection and
107
replication are the same in a plaque assay and a TCID50 plate then for any number
expressed as TCID50, the following expression would be correct.
𝑃(0) = 0.5 (3-3)
This would then mean that,
𝑒(−𝑚) = 0.5 (3-4)
Therefore,
𝑚 = −𝐼𝑛 0.5 = 0.693147 (3-5)
A value of 0.69 has been used in all infectivity calculations in this thesis to estimate
pfu/mL as follows.
𝑇𝐶𝐼𝐷50
𝑚𝐿 𝑥 0.69 =
𝑝𝑓𝑢
𝑚𝐿 (3-6)
The TCID50 was performed by dispensing 10-fold sample dilutions down a 96-well
plate onto CV-1 cells seeding at a density of 8000 cells/well. The plates were then
incubated at 37 ⁰C, 5% CO2 for 6 days and read by visually inspecting for cytopathic
effect (CPE) using a Leica DMIL LED bright field microscope (Leica Microsystems
Inc, IL USA) as well as visually inspecting for red fluorescence protein (RFP). An RFP
108
gene had previously been inserted into the backbone of the virus under investigation;
this was driven by a vaccinia virus (H5) promoter. TCID50 generated from inspecting
for CPR and RFP were equivalent as H5 is a stable promoter and vaccinia is unlikely to
drop genes the size of RFP (approx. 401 bp).
3.4.2 Quantification of total viral genomes by qPCR
Relative total intact virus particles were measured using quantitative real time
polymerase chain reaction, (qPCR) with Custom TaqMan® QSY
® probes (Thermo
Fisher Scientific Inc. Waltham, MA, US) which contains a QSY® quencher and FAM
®
dye.
The virus sample was diluted 1:10 in 20mM Tris buffer 2mM MgCl pH 8 and treated
with 350 units of Benzonase® for 4 h at 37⁰C. Samples were then incubated at 90⁰C for
1 hour in order to inactivate the nuclease and partially lyse the virus. Viral DNA was
then purified using the DNeasy Blood and Tissue kit (Cat# 69504 + 69506) (Qiagen,
Venlo, Limburg, Netherlands) in accordance with the manufacturer’s instructions. The
protocol used was for non-nucleated blood.
The total qPCR reaction volume was set to 50µL per reaction, 20 µl of template DNA
was used. Forward and reverse primer concentrations per reaction were 900nM and
probe concentration was 200nM.
Sequence data was as follows:
Probe: 6-FAM-ATTTTAGAACAGAAATACCC-MGB
Primers: Sense 5’-AACCATAGAAGCCAACGAATCC-3’
Antisense 5’-TGAGACATACAAGGGTGGTGAAGT-3’
109
A standard curve was generated using chromatography load material purified DNA.
qPCR recovery data is relative to the concentration of DNA in the chromatography load
samples.
Figure 3.4 qPCR standard curve generated from using chromatography load material
3.4.3 Quantification of total protein
The bicinchoninic (BCA) protein assay (Thermo Fisher Scientific Inc. Waltham, MA,
US) was used to assay total protein. Samples were analysed in accordance with the
manufacturer’s instructions.
This assay works on 2 key principles. The first is the chelation of copper with protein in
alkaline conditions in the presence of sodium potassium tartrate known as the biuret
reaction. This results in the formation of a light blue complex. During this reaction Cu2+
ions are reduced to Cu+. In the second reaction, two molecules of BCA form a complex
with each monovalent cation to from a purple complex which strongly absorbs light at
262 nm. As the levels of Cu2+
reduction is proportional to the levels of protein in the
y = -1.537ln(x) + 24.114 R² = 0.9997
17.00
18.00
19.00
20.00
21.00
22.00
0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00
CT
Percentage Recovery (Relative to Chromatography Load)
110
sample, a linear standard curve can be generated and used to estimate protein
concentrations of unknown samples.
Bovine serum albumin (BSA) was used as a standard for both the generation of a
standard curve, between 0 and 2000 µg/mL, and for spiking samples with known
quantities of protein to demonstrate the reliability of the assay. The diluent used was 50
mM sodium phosphate (NaH2PO4) pH 7.0 and all samples were diluted at least 4 times
to ensure appropriate buffer conditions and appropriate protein concentration. All
samples were measured in duplicate. The lower limit of quantification (LLOQ) is 20
µg/mL. 40 µg of BSA standard was used to estimate spike recovery.
Micro BCA was used when LLOQ was reached using BCA. Samples were analysed
according to manufacturer’s instructions. A standard curve was generated between 0
and 200 µg/mL, LLOQ is 2 µg/mL.
3.4.4 Visualisation of total protein by SDS-PAGE
3.4.4.1 SDS – PAGE
Non-reducing SDS-PAGE was used to visualise the total protein in selected
chromatography fractions. 10µl of each fraction was diluted in NuPage MES loading
buffer containing SDS. Precast NuPage Tris-Bis gels where used with MES running
buffer in an XCell SureLock Mini gel tank and run at 100V for 40 min. The protein
ladder used was the thermo scientific page ruler plus pre-stained protein ladder. All
materials were purchased from Thermo Fisher Scientific (Thermo Fisher Scientific Inc.
Waltham, MA, US).
111
Gels were initially strained with SimplyBlue SafeStain, which is a safe Coomassie G-
250 stain for proteins; however protein bands in the elution were too low in
concentration to be visible. A silver staining protocol was therefore used.
3.4.4.2 Silver staining protocol
1. Gels were fixed in 500mL of 40% methanol, 10% Acetic acid, in MilliQ for 1 h.
2. Washed in MilliQ 3x for 5min each.
3. Incubated in sensitizing buffer consisting of 5% (w/v) of sodium thiosulfate, 17g
sodium acetate and 75mL of ethanol in 250mL with water (MilliQ) for 30 min.
4. Washed in MilliQ 3x for 5min each.
5. Silver stained in 2.5% (w/v) silver nitrate in 250mL of MilliQ for 20min.
6. Washed in MilliQ for 45 s.
7. Developed in sodium carbonate (12.5g) with 200 µL of formaldehyde in 500 mL
of MilliQ until bands appears in elution fraction.
8. Stopped in EDTA-Na2 (3.65g) in 250 mL of MilliQ for 20min.
9. Washed in MilliQ 3x for 5 min each.
10. Preserved in 5% Acetic acid at 2-8oC until imaging.
3.4.5 Quantification of total dsDNA
The dsDNA quantification was done using the Quant-iTTM
PicoGreen® dsDNA reagent
from Molecular Probes Inc. (Cat. # P7581, Eugene, OR, US). Samples were analysed in
accordance with the manufacturer’s instructions. Lambda DNA (Cat. # D1501,
Promega Corporation, Madison, WI) was used as a standard for both the generation of a
112
standard curve, between 0 and 2000 ng/mL, and for spiking samples with known
quantities of DNA to demonstrate the reliability of the assay. The diluent used for all
dilutions was Tris EDTA (TE) and all samples were diluted at least 4 times (and up to
200x) to ensure appropriate buffer conditions such as removal of salt and appropriate
DNA concentration. All samples were measured in duplicate. The lower limit of
quantification (LLOQ) is 250 pg/mL. 40 µg of λ DNA standard was used to estimate
spike recovery.
PicoGreen® is a proprietary asymmetrical cyanine dye, similar to SYBR Green I
(Blotta et al. 2005). The dye binds specifically to dsDNA has an excitation wavelength
of 485nm and emission wavelength of 530 nm.
3.4.6 Calculation of total protein and total DNA purification factor
A purification factor is a useful way to show the level of clearance of process related
impurities such as DNA and total protein in relation to the product. This was calculated
according to the equation below.
𝑃𝐹 =(𝐶𝐷𝑁𝐴𝑥 𝑉𝐿𝑜𝑎𝑑)/𝑑𝑜𝑠𝑒
(𝐶𝐷𝑁𝐴𝑥 𝑉𝐸𝑙𝑢𝑡𝑖𝑜𝑛)/𝑑𝑜𝑠𝑒 (3-7)
where a dose is assumed to be 1x109 pfu and PF is purification factor. CDNA is the
concentration of DNA in ng/mL and VLoad is the volume of the load in mLs and VElution
is the volume in the elution in mLs. The PF for total protein was calculated in the same
way in each case by substituting concentration of DNA for the concentration of total
protein.
113
3.4.7 Quantification of total particles
3.4.7.1 Nanoparticle tracking analysis (NTA)
NTA (Malvern Instruments Ltd, Malvern, UK) was used to analyse viral particle
distribution and total particle concentration. A NanoSight LM10 model was used in all
experiments.
The camera level was set to 11 and the screen gain was set to 1. During processing of
the video frames, the detection threshold was set either 3 or 5 (stated in figures). Each
sample was analysed 5 times and the mean concentration was processed in all
evaluations.
Samples were first diluted to within the dynamic range of the assay (1x106
– 1x109
pfu/mL) using phosphate buffer without salt at pH 7. Dilutions made were typically
between 10 and 100. Using a 1 mL syringe the sample was then loaded into the LM10
chamber and progressed manually after every reading. In order to prevent any additional
dilution in the LM10 chamber, the optical flat was cleaned and dried between all runs
using water and compressed air.
114
3.4.7.2 Tunable Resistive Pulse Sensing (TRPS)
The qNano, produced by (IZON, Oxford, UK) was used to measure chromatography
load and elution fractions in order to determine levels of polydispersity as well as to
determine an approximate Zeta potential.
The system was calibrated with 200 nm ceramic beads (ID # CPC 200) in 300 mM
NaCl diluted 1000 times.
30-40 µL of virus sample was diluted 1000 times in PBS and loading into the top of the
qNano using a p200 pipette. TRPS can measure the concentration of particles between
concentrations of 1x105 – 1x10
12 particles/mL.
3.4.8 Visualisation of virus and contaminants using electron microscopy
3.4.8.1 Transmission electron microscopy (TEM)
Virus was fixed with 0.5% glutaraldehyde before being loaded onto carbon coated
copper grids. 20 µL droplets were left to partially dry at room temperature, washed with
deionised water, and then stained with 2% uranium acetate solution. Grids were
prepared in triplicate, and loaded into a Jeol 1010 transition electron microscope (with
digital image capture).
3.4.8.2 Scanning electron microscopy (SEM)
Virus was fixed and loaded onto carbon coated copper grids in the same way as is
described in Section 3.4.8.1. For SEM analysis grids were not negatively stained with
uranium acetate. Once dry, grids where coated with gold palladium using a Gatan high
115
resolution gold beam coater. Grids were loaded into a Jeol 7401 high resolution field
emission scanning electron microscope.
3.4.9 Statistical analysis
Throughout this work infectious virus concentration, total particle concentration and
residual DNA and protein concentration measurements were performed in either
duplicate or triplicate. In order to calculate error in these measurements relative
standard deviation (RSD) was calculated as follows:
The STDEV.S function in excel was used to calculate sample standard deviation, and
the sample relative standard deviation. This was under the assumption that the assay
measurements taken did not represent the entire population. The following equation is
used to calculate sample standard deviation.
𝑠 = √1
𝑛−1 ∑ (𝑥𝑖 − �̅�)2𝑛
𝑖=0 (3-8)
where n is the total number of replicates for a single sample, xi is the value of each
replicate and �̅� is the mean of all the replicates.
The RSD was then calculated as follows.
𝑅𝑆𝐷 =𝑠
�̅�× 100 (3-9)
Multivariate analysis was run using SAS JMP in order to test for correlations between
multiple variables. The built in analysis tool uses the Pearson product moment
correlation coefficient as a measure of the linear relationship between two variables
(SAS JMP 12 2017a).
116
𝑟 =∑(𝑋−�̅�)(𝑌−�̅�)
√∑(𝑋−�̅�)2
√(𝑌−�̅�)2 3-10
This can be simplified and written as
𝑟 =𝑆𝑃𝑋𝑌
√𝑆𝑆𝑋 𝑆𝑆𝑌 3-11
where SPXY is the sum of products, SSX and SSY are the sum of squares for each
variable.
DoE was used to model the effects of pH and salt concentration for CIM SO3
monoliths. The R2
values presented in the analysis shows how much the variation in
response values can be attributed to the model (Rouiller et al. 2012). The R2 values were
calculated as follows:
𝑅2 = (𝑆𝑆𝑚𝑜𝑑𝑒𝑙
𝑆𝑆𝑡𝑜𝑡𝑎𝑙) 3-12
where SStotal, or total sum of squares, is the sum of the squared difference between the
response values and the sample mean. This is the total variation in the response values.
The SSmodel, or model sum of squares, is the difference between SStotal and the SSerror. It
represents the variability explained by the model. SSerror is the error sum of squares and
represents the sum of the squared differences between the fitted values and the actual
values. This represents the variability unexplained by the fitted model (SAS JMP 12
2017b).
Throughout this thesis, the adjusted R2 has been used, which adjusts the R
2 value for the
number of parameters in the model. The Adjusted R2
𝑅2𝐴𝑑𝑗 = 1 −𝑀𝑒𝑎𝑛 𝑆𝑞𝑢𝑎𝑟𝑒 (𝐸𝑟𝑟𝑜𝑟)
𝑆𝑆𝑡𝑜𝑡𝑎𝑙 𝐷𝐹𝑡𝑜𝑡𝑎𝑙⁄ 3-13
117
where DFtotal is the degrees of freedom for the model and the error. The mean square for
error is the sum of squares divided by its associated DF. It represents an estimate of
population variance.
In a fitted model the residuals, or difference between the predicted responses, 𝑌𝑚𝑜𝑑𝑒𝑙 ,
and the measured observations, xi, is calculated in JMP using the root mean square
error (RMSE). This should be a low as possible.
𝑅𝑀𝑆𝐸 = √∑ (𝑌𝑜𝑏𝑠−𝑌𝑚𝑜𝑑𝑒𝑙)2𝑛
𝑖=0
𝑛 3-14
3.5 Sample handling
Sample handling is a very important part of virus bioprocessing.
Vaccinia virus stability will be discussed in some detail in Chapter 4. From the offset of
experimentation, it was decided that all samples should be treated in as close to the
same way as possible so as not to introduce any variability in virus infectivity as a result
of handling.
During every experiment, samples were taken of each fraction or stage needing to be
recorded. Samples were then aliquoted into smaller volumes for each assay needing to
be performed and all samples were left on the bench at room temperature until all
experimental samples had been taken. All samples from an individual experiment, such
as a chromatography run, were then frozen together at -80 ⁰C until required.
118
Chapter 4
Material Characterisation
4.1 Introduction
Initial material characterisation is a very important first step when developing viral
bioprocesses. Upstream processes are inherently variable thus an understanding of how
much batch to batch variability is likely and why it occurs is crucial if its effects on the
downstream process are to be understood.
In many viral processes infectivity is a major critical quality attribute (CQA).
Significant amounts of process knowledge and understanding need to come from
infectivity assays, which, as discussed in the previous chapter, can have an error range
of up to an order of magnitude. This makes it difficult to accept trends taken at face
value unless the experiments can be repeated and trends identified many times. Often,
due to time and material constraints, this is simply not possible without the availability
of optimised high throughput processes and accompanying analytics.
One way to combat this is to identify trends by using more than one analytical
technique. These techniques need to be orthogonally coupled with infectivity data in
order to generate a “picture” of what is happening in the system under evaluation. What
is often challenging is that each technique provides a specific angle of insight into the
often complex system, so they can often be misleading when individually evaluated.
In this chapter, characterisation of the viral harvest and chromatography load material
will be discussed in detail. The stage in the process in which this material is generated is
119
indicated in Figure 3.1, a process flow diagram is shown from cell culture to
chromatographic capture.
4.2 Harvest titre and residual impurity variability
In an attempt to understand batch to batch variability in vaccinia virus harvest material
the infectious titre, DNA concentration, and total protein concentration were measured
across a number of batches. Figure 4.1 shows the raw data generated. The distribution in
infectious titre is Gaussian and has a mean value of 1.57x109 pfu/mL. Infectious virus
was measured across 11 batches, DNA concentration measured across n=6 batches and
protein concentration measured across n=3 batches. The distribution incorporates the
variability in the analytics as well as inherent variability in the cell culture and primary
recovery processes. Figure 4.2 reiterates these points, showing the range in the
infectious viral titre, DNA, and total protein concentrations recorded in the harvest
material.
0 4 8 1 2
0
1 .01 0 9
2 .01 0 9
3 .01 0 9
0
5 0 0 0
1 0 0 0 0
1 5 0 0 0
2 0 0 0 0
2 5 0 0 0
B a tc h e s
Infe
cti
ou
s V
iru
s T
itre
(pfu
/mL
)
[Pro
tein
] (g
/mL
)
[DN
A] (n
g/m
L)
[V a c c in ia ]
[D N A ]
[P ro te in ]
Figure 4.1 Variability in infectious titre, DNA, and Protein concentration in vaccinia virus
harvest material post freeze thaw and centrifugation measured across 11 batches.
120
[V a c c in ia ] [D N A ] [P ro te in ]
0
1 .01 0 9
2 .01 0 9
3 .01 0 9
0
5 0 0 0
1 0 0 0 0
1 5 0 0 0
2 0 0 0 0
2 5 0 0 0
H a rv e s t c o n c e n tra tio n
Infe
cti
ou
s V
iru
s T
itre
(pfu
/mL
)
[Pro
tein
] (g
/mL
)
[DN
A] (n
g/m
L)
Figure 4.2 Infectious titre, DNA, and total protein concentration range in the harvest
material. (Vaccinia titre was measured across n=11 batches, DNA concentration measured
across n=6 batches and protein concentration measured across n=3 batches.
When taking a closer look at the data in Figure 4.1, there appears to be a correlation in
the concentrations of infectious virus and the total DNA in the harvest material. This
trend is also shown in Figure 4.3 after running a multivariate analysis on the raw data.
The blue shaded area shows a 95% density eclipse assuming a bivariate normal
distribution between each pair of variables. The red line and shaded area shows the
fitted regression line and a 95% confidence interval for the fitted regression. The
correlation between the virus and DNA concentration is shown to have an r value of
0.8614. The value summarises the strength of the linear relationship between each pair
of responses. The correlation between DNA and protein (r = 0.4029) and infectious
virus and protein (r = 0.6106) is less significant. The R value is calculated using the
Pearsons product moment correlation coefficient as shown in Equation 3-10.
121
Figure 4.3 Scatterplot matrix showing correlations between variations in concentration of
virus, DNA and protein in vaccinia harvest material.
It is possible that this may be linked to the number of cells per batch at the point of
infection varying from batch to batch, due to the methods used to estimate the total
number of cells per flask. It has been suggested in the literature when discussing
indirect measurements for determining cell expansion efficiency that glucose, lactate or
DNA concentration may be useful when dealing with cell culture systems in which
direct measurements of cell concentration are challenging, such as in hollow fibre
bioreactors (Wung et al. 2014). The reason this methodology was not evaluated in this
case, was that significant optimisation would have been required as well as an increased
amount of manual handling. Cell counting was performed either by using a TC20TM
automated cell counter (Bio-Rad Laboratories, CA, USA) or a haemocytometer. The
In fe c t io u s v i ru s
c o n c e n t r a t io n
(p fu /m L )
D N A
c o n c e n t r a t io n
(n g /m L )
p r o te in
c o n c e n t r a t io n
(g /m L )
In fe c t io u s v i ru s
c o n c e n t r a t io n
(p fu /m L )
D N A
c o n c e n t r a t io n
(n g /m L
p r o te in
c o n c e n t r a t io n
(g /m L
122
total number of cells in a single T175 flask was then assumed to be the same across all
flasks seeded at the same cell density (of ~1x106 cells/flask). The total number of cells
per batch prior to infection was then calculated as the number of cells in a single T175
flask multiplied by the total number of flasks used. Batches of cells were infected with
vaccinia once they became ~80% confluent.
4.3 Determination of vaccinia harvest Zeta Potential
The zeta potential of the viral harvest material was measured using a QNano devise
designed by (IZON, Oxford, UK). The Zeta potential of each particle is calculated from
the electrophoretic mobility using the Smoluchowski equation. The data is shown in
Figure 4.4, and suggests that the majority of particles have a negative zeta potential of
approximately -30mV. Similar to NTA the QNano is not able to measure particles of
larger than 1µm depending in the nanopore used. This data is in agreement with
literature values for vaccinia virus in terms of its isoelectric point, which is between 4
and 5 (Michen & Graule 2010).
123
Figure 4.4 Zeta potentials of vaccinia harvest measured using the QNano showing
calculated zeta potential values for individual particles. Average value shown to be -32
mV.
4.4 Virus stability
Initially stability studies were performed in order to identify critical process parameters
that might have an impact on the recovery of infectious virus during chromatography.
The first study shown in Figure 4.5.A was designed to test whether vaccinia harvest
material was stable at room temperature, as typical downstream unit operations are
conducted without thermoregulation. A 1 mL aliquot of vaccinia harvest was left inside
a biosafety level two (BSLII) cabinet and assayed for infectious virus every hour.
The mean data shows an initial drop in the number of infectious particles, although the
assay shows high variability in initial measurements. After the first 2 hours, the
infectivity remains constant. After running an ordinary one-way ANOVA assuming a
Gaussian distribution, no significance was found if a P value cut of 0.05 is used. (P
3 0 0 6 0 0 9 0 0
-4 5
-3 0
-1 5
0
1 5
3 0
4 5
P a r t ic le s iz e (n m )
Ze
ta P
ote
nti
al
(mV
)
124
value = 0.0605, R2 = 0.5233) On this basis, it was concluded that vaccinia harvest is
likely to be relatively stable at room temperature.
The second experiment shown in Figure 4.5.B was designed to test whether increasing
the salt concentration would have an impact on the infectivity of the chromatography
load material. 50 mM sodium phosphate buffer was made up at pH 7 ranging from 0 to
2M NaCl. Samples were incubated for 2 hours and then assayed for infectious virus.
The rational for analysing viral stability in different salt concentrations was that typical
load and elution buffers used for IEX and HIC chromatography vary in both
concentration and type of salts required. It was decided that any understanding of the
edges of failure in terms of loss in infectivity was important.
The concentration of infectious virus is at its highest without NaCl. When 0.25 M NaCl
is added, the total number of infectious viral particles decreases by almost 30%;
however, as the concentration of NaCl further increases, from 0.25-2 M, the
concentration remains constant.
As a control and comparator, the stability of virus in cell culture media (DMEM)
containing 5% FBS and hydrophobic interaction chromatography load buffer containing
ammonium sulfate was also tested. Samples diluted in media (DMEM) were assayed at
t=0 h and t=2 h and the concentration was not found to increase significantly. Given
this, it can be inferred that vaccinia virus infectivity, both in media and sodium
phosphate without NaCl, is relatively unaffected over a 2 h incubation at room
temperature. There does appear to be an initial drop in infectivity in sodium phosphate
buffer containing NaCl at concentrations typically seen in chromatographic separations.
However, due to assay variability being as high as it is, this difference was not found to
be statistically significant (P = 0.2030, R2
= 0.4314).
125
0 1 2 3 4 5 6 7 .5
0
2 .01 0 0 9
4 .01 0 0 9
6 .01 0 0 9
8 .01 0 0 9
1 .01 0 1 0
T im e
(h o u r s )
Nu
mb
er o
f I
nfe
cti
ou
s P
art
icle
s
(pfu
/mL
)
0.0
0 M
Na
Cl
0.2
50
M N
aC
l
0.5
00
M N
aC
l
0.7
50
M N
aC
l
1.0
0 M
Na
CL
2.0
0M
Na
Cl
1M
AS
2M
Na
Cl
DM
EM
(5
% F
BS
)
0
2 .01 0 8
4 .01 0 8
6 .01 0 8
C o n d i t io n s
Co
nc
vir
us
Lo
ad
(pfu
/mL
)
A
B
Figure 4.5 A) Vaccinia virus harvest stability time trail at room temperature. B) Vaccinia
stability study with increasing NaCl concentration in phosphate buffer pH 7 at room
temperature. Points refer to raw TCID50 replicate values (n=3), mean values shown as a
horizontal line for each condition.
126
A design of experiments (DoE) was then performed in order to analyse the combined
effects of both NaCl and pH on virus stability, again at room temperature, over a 2 hour
period, in 25 mM sodium phosphate buffer. Given the inherent variability in the TCID50
measurements, fitting correlations to the data is challenging. The ANOVA showed a
significant individual P value for effects due to NaCl concentration (P = 0.0048). The P
value for effects due to pH was relatively low, but above the 0.05 cut often used to
demonstrate significance (P=0.0541). However, after fitting a partial least squared
model to the dataset the adjusted R2
value was only 0.31, and the residuals were high,
the RMSE was 1.453x109.
The data is shown in Figure 4.6. Each triplicate TCID50 reading is shown as a 3D
histogram rather than the modelled data being shown as a response surface plot. There
appears to be an apparent increase in stability as pH and NaCl concentration increases.
It should be noted that the material used was purified by density gradient
ultracentrifugation prior to analysis and so would have contained less DNA and host
cell protein. This was due to material availability.
Compared to the data in Figure 4.5 this result does seem to suggest a slightly different
trend in terms of the effects of NaCl concentration on infectious virus stability;
however, in both cases the variability is around 30%. Under close inspection, this
variability appears even higher at high NaCl concentration and pH. While the material
in the DoE appears slightly more stable as NaCl and pH increase, the control samples
left in DMEM media remained fairly constant at 8.16x109 and 1.41x10
10 pfu/mL
respectively. This shows that the sample diluted in sodium phosphate buffer without
NaCl recovered only 5% of the infectious particles after 2 hours at low pH.
As aforementioned, robust conclusions are difficult to generate when dealing with
highly variable datasets. When considering future chromatographic development it
127
seems fair to assume that increasing the NaCl concentration will unlikely cause vaccinia
infectivity to drop significantly. This makes both ion exchange and hydrophobic
interaction chromatography possible candidates when screening different chemistries.
As expected, low pH seems to have a negative effect on infectivity. Therefore, when
optimising loading and elution conditions higher pH values have generally been used.
Figure 4.6 3D histogram showing raw data values from vaccinia virus stability factorial
design. Virus was purified using 2 consecutive sucrose density gradient ultracentrifugation
runs prior to analysis. Study was performed at room temperature over a 2 hour period in
25mM Sodium phosphate buffer. Replicate values (n=3) have been jittered by shifting the
pH by 0.1 units. A control was run in DMEM media containing 5% FBS at the beginning,
t=0, and after 2 hours t=1, the concentration of the control samples were 8.16x109 and
1.41x1010
, respectively.
128
4.5 Physical characterisation
While infectivity and residual impurity levels are vital to obtain in order to understand
process outputs such as efficiency and productivity, not to mention being required for
final product release, physical characterisation is also important.
There are a number of techniques available that have either been designed or
implemented to study viral integrity, morphology and characteristics such as levels of
polydispersity. In this study vaccinia was analysed using transmission and scanning
electron microscopy as well as NTA in order to investigate levels of aggregation.
While it is unknown whether viral aggregates need to be considered as a critical quality
attribute, it is considered possible that vaccinia aggregation may have an effect on
process yield. Data from the literature also suggests that viral aggregation can influence
infectivity measurements (Floyd and Sharp 1979). For these reasons it was decided that
possible process parameters that could cause or prevent aggregation should be
evaluated.
4.5.1 Size distribution using nanoparticle tracking analysis (NTA)
The size distribution of the chromatography load material overlaid with a null prep
sample as measured using a NanoSight (Malvern Instruments Ltd, Malvern, UK) is
shown in Figure 4.7. The null prep contained uninfected cell lysate prepared in the same
way as the virus load. Both samples were diluted in 50mM sodium phosphate buffer pH
7 (1:30) prior to analysis to ensure that the concentration of particles was within the
correct working range for the instrument as specified by the manufacturer, (between
1x106 – 1x10
9 particles per mL).
129
A bimodal distribution is evident in both the load and the null prep samples. Both peaks
can be seen to overlay (Figure 4.7) but are at a lower concentration in the null prep than
in the virus load. It is speculated that host cell components and degraded viral particles
make up the first peak in the load fraction. Vaccinia virus is around 300nm in diameter,
which approximately corresponds to the size range of the second peaks. It is likely,
therefore, that this population in the load sample contains intact viral particles. The fact
that there is a second peak in the null prep sample leads us to the conclusion that there
are particles of a similar size range to vaccinia generated during the primary recovery
step. Based on this observation, it is expected that the NTA will overestimate the
number of virus particles in the load fraction. Similarly, it would likely underestimate
the recovery of virus across the column, assuming that a proportion of 300 nm particles
derived from the host cell will be removed during chromatography.
0 3 0 0 6 0 0 9 0 0
0
2 .01 0 8
4 .01 0 8
6 .01 0 8
8 .01 0 8
1 .01 0 9
P a rtic le s iz e
(n m )
Nu
mb
er
of
Pa
rtic
les
L o a d
N u ll P re p
Figure 4.7 Average size distribution data (n=5, 1min tracks, detection threshold 3). Load is
vaccinia virus harvest material post dilution post filtration. Null prep is harvest material
post dilution post filtration without virus, up and down stream steps treated in the same
way throughout.
130
The effects of high and low salt concentration on particle size distribution were then
tested, again using NTA. As this thesis is focusing on the development of a
chromatographic process, it was decided to use IEX and HIC load buffers in the
evaluation.
Figure 4.8 shows the size distribution of the chromatography load material when diluted
in cation exchange (CIEX) load buffer, 50mM sodium phosphate pH 7, and HIC load
buffer 50mM sodium phosphate 1M ammonium sulfate 2M NaCl pH 7. The results
show that buffer containing ammonium sulfate causes an increase in polydispersity.
NTA is unable to identify particles larger than 1000 nm, which may result in some
inaccuracy. However, it appears that the profile when diluted in high salt shifts to the
right, suggesting self-association of particles in the sample. There also appears to be a
slight increase in the number of particles at approximately 150 nm. The reason for this
is not clear, however, it is speculated to be due to the aggregation of smaller particles
such as fragments of DNA or proteins in the load that has previously been below the
dynamic size range of the instrument. As it is unknown from looking at this data
whether it is the virus material, contaminants, or a combination of both that are
aggregating, the experiment was repeated and analysed using electron microscopy in
order to gain more information.
131
0 3 0 0 6 0 0 9 0 0
0
2 .01 0 8
4 .01 0 8
6 .01 0 8
8 .01 0 8
1 .01 0 9
P a r t ic le S i z e
(n m )
Nu
mb
er
of P
arti
cle
s L o a d
1 M (N H 4 ) 2 SO 4 2 M N a C l
L o a d
N o S a lt
E lu tio n
Figure 4.8 Effects of diluent salt concentration on particle size distribution. (n=5, 1min
tracks, detection threshold 5) Harvest material post dilution post filtration was diluted
1:30 times in 50mM NaH2PO4 pH7 and 50mM NaH2PO4 1M (NH4)2SO4 2M NaCl pH7
respectively.
4.5.2 Visualisation of intact particles and evaluation of visible impurities
Information in the literature does agree with initial findings from NTA data. It suggests
vaccinia virus is prone to aggregate, especially in impure preparations (Kim and Sharp
1966).
Data generated using transmission and scanning electron microscopy was compared in
order to visualise the extent of virus aggregation in DMEM media containing 5% FBS,
IEX loading buffer, and as previously mentioned HIC loading buffer conditions. This
was then compared to the NTA data previously discussed.
Images generated from electron microscopy are not quantitative. They represent an
extremely small fraction of a sample and require a partial drying and staining or gold
132
coating step depending on the methodology used. Images can therefore only be used to
aid in our understanding of more quantitative measurements.
Images generated from impure preps of vaccinia virus in both DMEM and sodium
phosphate buffer without salt, appear to show relatively high levels of aggregation, as
shown in Figure 4.9 and Figure 4.10. Viral clusters were evident across all grids
analysed (n=3 for each sample). Stability data in Figure 4.5.B indicates that virus is
relatively stable under both of these conditions. Data in Figure 4.5.A indicates that
variability in infectivity is higher at t=0 h than at later time points. This may be due to
aggregation. Floyd and Sharp showed that aggregation can impact on infectivity of both
polio and reoviruses, (Floyd & Sharp 1979). If aggregation is time dependant, this may
explain this initial variation.
A TEM image, at high NaCl and ammonium sulfate concentration can be seen in Figure
4.11. Virus is shown to be positively stained; perhaps due to the size of the clusters
formed, nevertheless a large amount of aggregation is clearly visible. When compared
to Figure 4.9, which is at the same magnification of 15,000x, the level of aggregation
looks significantly higher. As it is difficult to make out individual particles, the sample
was also analysed using SEM. This data can be seen in Figure 4.12 and Figure 4.13.
Particles of a similar size to vaccinia virus can be seen in Figure 4.12 to form extremely
large complexes. The aggregates formed are larger than 6 µm across and appear to be
made of many virus like particles. It is speculated that these aggregates in reality are
made up of other contaminants as well as vaccinia. This hypothesis is partially backed
up in Figure 4.13. A smaller virus aggregate appears to be associated with another
material. The image shows two proposed vaccinia particles with an associated structure
possible resembling DNA complexed with positively charged histone proteins. It is
speculated that this may represent heterochromatin as the structure appears to resemble
133
the “beads on a string” model often used to describe the complex between DNA and
histone proteins. It is well documented in the literature as discussed (Wolff & Reichl
2008) that the removal of DNA from viral feed streams is challenging. These
observations, may partially explain why this is the case. DNA is likely to be difficult to
remove as a result of the complexes it forms with virus particles. This view was also put
forward and addressed in (Jordan et al. 2015).
134
Figure 4.9 Vaccinia virus harvest material in DMEM 5% FBS 15000x magnification, scale
bar reads 1µm
135
Figure 4.10 Vaccinia harvest diluted in CIEX load buffer 8000x magnification,
scale bar reads 2µm.
136
Figure 4.11 TEM micrograph of Vaccinia harvest in HIC load buffer 15000x
magnification, scale bar reads 1µm
137
Figure 4.12 SEM micrograph of vaccinia virus material diluted in HIC load buffer 10000x
magnification, scale bar reads 1µm Image shows an extremely large aggregated molecules
potentially made up of many associated vaccinia virus particles. Rod-like structures are
thought to be ammonium sulphate crystals.
138
Figure 4.13 SEM micrograph of vaccinia virus material diluted in HIC load buffer, 25000x
magnification, scale bar reads 1µm
139
4.6 Conclusions
Batch to batch variation in infectious viral titre as well as residual impurity
concentration has been investigated in this chapter along with virus stability,
morphology, and aggregation.
The use of a variety of orthogonal analytical methods has allowed a number of
conclusions to be drawn that are important to take into account when developing
downstream unit operations. These conclusions are that:
Vaccinia virus infectivity is relatively unaffected by elevating NaCl and
ammonium sulfate concentrations, but appears to drop off over time at room
temperature, especially at low pH.
High levels of aggregation have been identified in impure viral preparations,
especially at elevated ammonium sulfate concentration.
High batch to batch variability in infectious titre and residual DNA and protein
concentrations identified, possibly due to variation in the total number of cells
prior to infection per batch as well as variability in the TCID50 assay.
A full characterisation of viral starting material is a never ending process. What is
important is to generate enough product knowledge and understanding to be able to
generate and work to understand mass and particle balances generated during process
development activities. Without this information troubleshooting activities become
impaired, and the generation of robust conclusions on process performance, ultimately
contributing to patient safety, is impossible.
140
Chapter 5
Ion Exchange Chromatography Process
Development
5.1 Introduction
As described in Section 2.4.3, ion exchange (IEX) monolith chromatography, and
especially anion exchange (AIEX), has been demonstrated in the literature as being
capable of capturing and purifying infectious viral particles, virus like particles (VLPs)
and other large macromolecular entities. The design of an efficient adsorption-based
separation process for vaccinia virus, however, is complicated by its large size and
complex physicochemical properties. Enveloped viruses in general are also known to be
fairly labile, which has further complicated the development of chromatographic
separations. An important constraint is to prevent loss in infectivity post purification.
The large size of vaccinia virus excluded many popular IEX adsorbents and attention
has been focused on large pore adsorbents. In this chapter, the process development of
vaccinia virus using three commercially available ion exchange monoliths for initial
capture and purification will be described. The aim was to generate a capture step able
to bind and elute infectious vaccinia virus from crude lysate removing double stranded
DNA (dsDNA) and proteins. The plan was to start with a screening study designed to
test a selection of IEX chemistries, CIM QA, CIM DEAE, and CIM SO3 (each with a
nominal channel diameter of 6 µm). The chemistries were chosen based on previously
published data on the use of CIM monoliths to purify viruses described in Section
141
2.4.3.2. It was decided that the most promising chemistry would then be taken forward
to start optimising load and elution strategies.
5.2 IEX Monolith Screening Study
Initial screening studies were primarily designed firstly to determine which IEX CIM
monolith chemistries vaccinia was able to adsorb to at neutral or weakly basic pH, and
then secondly to determine if there were any major differences in viral recovery or
impurity removal capabilities from any of the monoliths. Approximately 1x1010
pfu of
virus (10 mL fractions) were diluted 1:5 in IEX load buffer, as shown in Table 5.1, and
passed through a 0.8µm cellulose acetate syringe filter, as described in Figure 3.1, before
being loaded onto each column. The rationale for choosing the conditions outlined are
listed below:
1. Typically low pH is used to remove infectious viruses from biological products
(WHO 2004). It was thought that loading vaccinia at low pH would likely cause
unacceptable losses in infectivity.
2. Cell culture pH is around neutral (pH 7) therefore it was thought sensible to test
both CIM SO3 and CIM QA at pH 7. The rationale here is that vaccinia is likely
to be stable at physiological pH.
3. CIM DEAE was run at pH 8.5 as the capacity was speculated to be lower than
that of CIM QA at pH 7. HEPES was used in place of Tris for CIM QA when
run at pH 7
4. CIM QA was then also tested at pH 8.5 in order to compare directly with CIM
DEAE.
5. Tris has a pKa of 8.07 and is therefore cationic at neutral pH. pH 7 is also at the
bottom of its buffering capacity. Sodium Phosphate was used in place of Tris for
CIM SO3.
142
Table 5.1 DNA and total protein purification factors recorded during initial screening
studies on CIM SO3, CIM QA, and CIM DEAE. Buffer systems used are as shown, a
concentration of 50 mM was used in each case. Equilibration buffer did not contain NaCl.
Elution buffer contained 1.5 M NaCl. Purification factors calculated according to
Equation 3-7.
Monolith
Chemistry
Buffer System
(used for equilibration, wash,
and elution)
Protein Purification
Factor
DNA Purification
Factor
SO3 Sodium phosphate pH 7 7.63 0.58
QA Tris pH 8.5 2.09 0.18
QA HEPES pH 7 2.18 0.40
DEAE Tris pH 8.5 2.99 0.16
1 mL radial monolith columns were used in all experiments. The volumetric flow rate
used was 5 mL/min (linear velocity = 195.08 cm/h). A process flow diagram indicating
each unit operation from cell culture to chromatographic capture is shown in Figure 3.1.
Representative chromatograms for each chemistry are shown in Figure 5.1. UV
absorbance at 280 nm and conductivity values shown in mS/cm are presented for CIM
SO3, CIM QA and CIM DEAE monoliths.
143
Figure 5.1 Representative chromatograms from CIM S03 in Sodium Phosphate pH 7.0
(A), CIM QA in Tris pH 8.5 (B), CIM QA in HEPES pH 7.0 (C), and CIM DEAE in Tris
pH 8.5 (D).
0 2 0 4 0 6 0 8 0 1 0 0
0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
0
5 0
1 0 0
1 5 0
C o lu m n V o lu m e s
mA
u
(28
0 n
m)
Co
nd
uc
tivity
(mS
/cm
)
A b s o rb a n c e (2 8 0 n m )
C o n d u c t iv i ty (m S /c m )
0 2 0 4 0 6 0 8 0 1 0 0
0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
0
5 0
1 0 0
1 5 0
C o lu m n V o lu m e s
mA
u
(28
0 n
m)
Co
nd
uc
tivity
(mS
/cm
)
0 2 0 4 0 6 0 8 0 1 0 0
0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
0
5 0
1 0 0
1 5 0
C o lu m n V o lu m e s
mA
u
(28
0 n
m)
Co
nd
uc
tivity
(mS
/cm
)
0 2 0 4 0 6 0 8 0 1 0 0
0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
0
5 0
1 0 0
1 5 0
C o lu m n V o lu m e s
mA
u
(28
0 n
m)
Co
nd
uc
tivity
(mS
/cm
)
A
B
C
D
L o a d
E lu t io nW a s h
L o a d
E lu t io nW a s h
L o a d
E lu t io nW a s h
L o a d
E lu t io nW a s h
144
Figure 5.2 A) Box and whiskers plot (median & range) showing infectious vaccinia
recovery data from CIM SO3, CIM QA, and CIM DEAE at pH 7 and pH 8.5. Red plots
show the percentage of infections virus in the flow through fraction relative to the load,
blue plots shows percentage of infectious virus in the elution fraction relative to the load,
and black plots show the percentage infectious virus in the elution minus the total number
of particles lost in the flow through relative to the load. B) Box and whiskers plot (median
& range) showing the number of viral particles loaded vs the capacity achieved on CIM
SO3, CIM QA, and CIM DEAE.
SO
3 p
H 7
QA
pH
8.5
QA
pH
7
DE
AE
pH
8.5
0
5 0
1 0 0
1 5 0
C h e m is ty , [S a lt] lo a d , p H
Infe
cti
ou
s V
iru
s R
ec
ov
ery
(%
)
I n fe c t io u s V iru s R e c o v e ry F lo w T h ro u g h (% )
In fe c t io u s V iru s R e c o v e ry E lu t io n (% )
C a lc u la te d In fe c t io u s R e c o v e ry A s s u m in g N o L o s s o f V iru s in F lo w T h ro u g h
SO
3 p
H 7
QA
pH
8.5
QA
pH
7
DE
AE
pH
8.5
SO
3 p
H 7
QA
pH
8.5
QA
pH
7
DE
AE
pH
8.5
0
1 .01 0 1 0
2 .01 0 1 0
3 .01 0 1 0
C h e m is try , [S a lt] lo a d , p H
Infe
cti
ou
s V
ira
l P
art
icle
s (
pfu
)
N u m b e r o f P a r t ic le s L o a d e d
C a p a c ity A c h e iv e d
A
B
145
After analysing the results, the vaccinia virus concentrations in each stream are shown
in Figure 5.2, it can be seen that vaccinia is able to adsorb to both AIEX and CIEX
monoliths even though it has a zeta potential of approximately -32 mV (Figure 4.4), and
an isoelectric point of between 4 and 5 (Michen & Graule 2010). After a review of the
literature it was found that viral adsorption onto IEX resins at both sides of their given
isoelectric point has been reported for a number of viruses before (Banjac, Roethl,
Gelhart, Kramberger, Jarc, Jarc, Strancar, Muster, & Peterka 2014;Wolff & Reichl
2011;Wolff, Siewert, Lehmann, Hansen, Djurup, Faber, & REICHL 2010b); however, a
definitive conclusion as to why it occurs so commonly has not been reached. As with
proteins, it is likely that viruses are sufficiently heterogeneous to form electrostatic
interactions on the "wrong" side of their isoelectric point (Yamamoto & Ishihara 1999);
however, complex interactions between charged viral particles and contaminating
impurities of opposite charge may also play a role, especially in crude feed streams.
The recovery of virus in the elution fractions for all IEX monoliths is shown to be
comparable indicating that the capacity of each column is similar. However, infectious
virus was also found in the flow through fractions, indicating that the columns may have
been overloaded (Figure 5.2A).
Residual impurity removal data is shown in Table 5.1. DNA purification factors can be
seen to be less than 1, indicating that the net recovery of infectious virus was lower than
the recovery of DNA relative to the load for each chemistry. Protein purification factors,
on the other hand, show some encouraging results. The AIEX monoliths (CIM QA and
CIM DEAE) are able to achieve a protein purification factor of between 2 and 3, while
the CIEX monolith (CIM SO3) achieved a protein purification factor of 7.63. This
suggests that the majority of proteins in the load are acidic. The experiments were not
designed to test for removal of residual DNA or protein, as the elution buffer NaCl
146
concentrations used were too high (1.5 M). This concentration was picked in order to
maximise the recovery of infectious virus.
Following on from the screening studies on IEX monoliths there are a number of
conclusions that can be drawn prior to moving on to the next stage in process
development.
1. DNA removal will likely be dependent on resolution being achievable in the
elution fraction.
2. Protein removal will likely by easier to achieve than DNA removal, as some
base-line clearance is seen even without elution buffer optimisation. This is
especially true for CIM SO3, as the majority of proteins loaded are recovered in
the flow through fraction and so do not bind to the column.
3. Vaccinia virus is able to adsorb to both CIEX and AIEX monoliths at neutral
pH.
4. Binding capacity and percentage recovery of infectious virus is similar across all
chemistries tested.
5. AIEX monoliths, especially CIM QA, appear to show a double breakthrough in
absorbance during the load (as seen in Figure 5.1.B & C). It is unlikely that this
phenomenon is due to vaccinia breakthrough, as it does not occur in any of the
CIM SO3 chromatograms. It is thought possible that it may be DNA and/or
protein as both adsorb to AIEX. The fact that QA is a stronger AIEX than
DEAE would also support this theory, since the double breakthrough occurs
earlier in DEAE suggesting a lower capacity.
147
It was decided at this point that only two chemistries would be taken to the next stage of
process development due to resource limitations. CIM SO3 was chosen as the main
candidate because of its potential to remove protein. More than 60% of the total protein
loaded onto CIM SO3 monoliths during the screening study was recovered in the flow
through fractions. It was speculated that this would allow elution step design to focus
on DNA removal and infectious yield. Based on the data from the screening study, it
was not possible to fairly distinguish between CIM QA and CIM DEAE as both showed
comparable recovery of infectious particles and achievable capacity as well as removal
of DNA and protein. As only one could be taken forwards, however, it was decided that
CIM DEAE would be put aside and CIM QA would be developed further. Due to the
presence of infectious virus in the flow through fractions from both CIM SO3 and CIM
QA it was decided that a dynamic binding capacity study (DBC) needed to be
performed. A DBC was completed on CIM SO3 (Figure 5.4D Run 1) and showed
breakthrough at approximately 3x109 pfu/mL of monolith. This was taken as an
approximate binding capacity for both CIM SO3 and CIM QA monoliths, based on the
capacities achieved in the screening studies (Figure 5.2B). The DNA and total protein
concentration in the virus load material, prior to running the DBC study, was ~1000
ng/mL and ~700 µg/mL, respectively.
148
5.3 Evaluation of DNA and Protein Elution Profiles from CIM SO3
and CIM QA Monoliths
A linear gradient elution was run on CIM SO3 as it can be seen in Figure 5.3.A and B
and CIM QA monoliths (Figure 5.3.C). The gradient was run over 20 CVs between 0-
1.5M NaCl in 50 mM sodium phosphate and 50 mM Tris for CIM SO3 and CIM QA,
respectively. Both columns were challenged with approximately 3x109 infectious viral
particles. 90% of the virus loaded adsorbed to each chemistry, with only 5-10 % of the
infectious virus found in the flow through fractions. The CIM SO3 chromatograms
(Figure 5.3.A and Figure 5.3.B) show that the majority of infectious virus is eluted
before the DNA. 40% of the DNA in the load is adsorbed to the column, and greater
than 90% is removed with a 50% recovery of infectious virus in the first 10CVs. Virus
starts to elute after 2 CVs, which corresponds to approximately 150 mM NaCl. DNA is
then co-eluted with a smaller viral peak after 10 CVs, corresponding to approximately
750 mM NaCl.
149
Figure 5.3 Data shows the infectious viral particles (pfu) mass of protein (µg) and mass of
DNA (ng) in elution fractions A) Linear gradient elution run on CIM SO3 monolith loaded
to approximately 3x109 pfu/mL run over 20 CV. B) Linear gradient elution run on CIM
SO3 monolith loaded to approximately 3x108 pfu/mL and run over 25 CV. Conditions for
CIM SO3 Buffer A: 50mM sodium phosphate pH7, Buffer B: 50mM sodium phosphate
1.5M NaCl pH 7. C) Linear gradient elution run on CIM QA monolith loaded to approx.
3x109 pfu/mL monolith run over 20CV between 0 and 1.5M NaCl. Condition for CIM QA
Loading Buffer: 50mM Tris pH 8.5. Elution Buffer: 50mM Tris 1.5M NaCl pH 8.5
150
The results indicate that CIM SO3 monoliths are able to resolve most of the virus from
the adsorbed DNA. CIM QA is not able to achieve this without heavy losses in virus
yield, as DNA co-elutes with the main infectious viral peak as seen in Figure 5.3.C. As
DNA was identified as a critical quality attribute (CQA), it was decided that the CIM
SO3 linear gradient should be repeated but that the column should be challenged with
an order of magnitude less infectious virus (approximately 3x108 viral particles per mL
of monolith) and eluted over a longer gradient (25 CVs rather than 20 CVs) in order to
maximise the resolution achievable, The results can be observed in Figure 5.3.B. It
shows the same trend and percentage removal of DNA across the elution peak as
illustrated in Figure 5.3.A.
The reason why virus and DNA co-elute towards the end of the gradient is unclear. It
has been speculated that this may be due to a complex interaction between the virus and
DNA molecules. SEM images of CIM OH fractions, presented in Figure 6.9, show virus
associated with long DNA-like structures resembling “beads on a string”. It has been
speculated that this structure may represent heterochromatin, which, due to the positive
charge of the histone complexes, might enhance the interaction between the virus and
the monolith. Other groups have also experienced difficulty in separating vaccinia virus
and DNA, and experimented by adding chaotropes to disrupt potential viral-DNA
complexes prior to nuclease treatment (Jordan, Weimer, Iarusso, Bernhardt, Lohr, &
Sandig 2015b).
A positive charge may also come from viral coat proteins even though the overall
charge across the surface is negative. It has been suggested, based on work with
influenza virus, which is also able to adsorb to CIM QA and CIM SO3 monoliths, that
buffer condition dependent variation in charge distribution across the viral surface may
151
give viral vectors enough net charge to bind to both cationic and anionic chemistries
(Banjac, Roethl, Gelhart, Kramberger, Jarc, Jarc, Strancar, Muster, & Peterka 2014).
Another possibility might be the presence of a cationic bridging molecule in the feed
stream that adsorbs to the stationary phase and is able to retain enough positive charge
to interact with the virus, DNA, and any viral-DNA complexes present. Based on the
screening studies, there is protein in the flow through fraction from both the CIEX and
AIEX monoliths, suggesting that while the majority of host cell proteins in the load are
acidic, there are also basic proteins present. The issue with this hypothesis is that it is
difficult to envisage a species of host cell protein that would be able to retain enough
net charge while bound to CIM SO3 to hold a large viruses like vaccinia on the surface.
What is evident from the data in Figure 5.3 is that DNA is more retained at high NaCl
concentration than the majority of infectious viral particles. This indicates that either
DNA on its own, or DNA complexed with other particles in the feed stream, forms a
stronger interaction with the column than the infectious viral particles on their own.
It is clear that complex mechanisms underlie the retention of vaccinia on these resins. In
the current context of developing a robust separation, it was decided at this point to
design a stepwise elution scheme for CIM SO3 as this resin appeared to be able to
separate infectious virus from contaminating DNA and protein.
5.4 Development of a stepwise elution schedule for CIM SO3
A central composite screening DoE was initially performed in order to sift through
factors affecting the removal of DNA whilst maximising the recovery of infectious
virus in the elution. Factors tested were sodium chloride concentration and pH. The load
152
challenge in each case was approximately 3x109 pfu/mL of monolith. Based on the
gradient elution results shown in Figure 5.3, ranges tested in the DoE were set between
250-750 mM NaCl pH 6-8 in sodium phosphate buffer. Initially only the corners of the
design space and centre points were tested, however, as there was a large amount of
noise in the TCID50 measurements, face centred and internal augmented points were run
in order to generate more information, specifically on infectious virus recovery.
Figure 5.4.A & B shows the effects of NaCl and pH on the removal of DNA and
recovery of infectious virus in the elution pool. DNA purification factors are used here
to show the effects of any trade-off between DNA removal and virus recovery.
Modelled data showing the effects of NaCl concentration and pH on the removal of
DNA is portrayed in Figure 5.4.C. The black solid lines show the modelled data points
while the blue dotted lines indicate a 95 % confidence interval. The red dotted lines
show the points on the graph where the recovery of infectious virus was shown in
Figure 5.4.B to be at its highest.
153
Figure 5.4 Data generated from CIM SO3 monoliths. A) 3D histogram illustrating the
effects of NaCl concentration and pH on DNA purification factors measured from each
run across a DoE central composite design. Raw data rather than modelled points are
shown. B) 3D histogram showing the effects of NaCl concentration and pH on infectious
viral recovery from each run across a DoE central composite design. Face centred and
internal axial points are also shown as raw data rather than modelled points. C) DoE
prediction profiler showing the modelled effects of NaCl concentration and pH on the
removal of DNA from the load. (NaCl concentration leverage, P = 0.0007, pH leverage, P =
0.0451, Adjusted R2
= 0.98) (n = 2). All TCID50 measurements were performed in
triplicate, DNA and protein values measured in dublicate, mean values shown. D) Two
dynamic binding capacity (DBC) runs on CIM SO3 with ~1000 ng and ~3000 ng of
DNA/mL in the load.
154
JMP Pro12 (SAS Cary NC USA) was initially used to model the data, and fitted a
quadratic function suggesting curvature in infectious virus recovery due to sodium
chloride concentration. The DoE suggested that 500 mM sodium chloride would give
the highest recovery from CIM SO3; however, due to the levels of inherent variability
in the TCID50 measurements, the adjusted R2 for the model fit was only 0.23. The
model P-Value was fairly small at 0.0476 indicating significance in the model fit for
curvature due to NaCl concentration, however, due to the size of the residuals (RMSE =
18.6) it was decided that conclusions on optimal CIM SO3 elution conditions should not
be drawn from the model. The raw data was therefore plotted without the regression as a
3D histogram. An optimum salt concentration required to elute infectious virus would
suggest a potential trade-off, perhaps between the number of viral particles desorbed
from the column, and the stability of vaccinia in the elution buffers. When referring
back to the stability data discussed in Chapter 4, there does appear to be some reduction
in infectivity in 50 mM sodium phosphate as NaCl concentration is increased from 0-
0.75 M. However, the statistical significance of this data is low making it difficult to
generate a conclusion. This is likely due to TCID50 assay variability. Although the
variation between replicates in Figure 5.4.B is high, there appears to be a general
increase in infectious virus recovery as NaCl concentration in the elution buffer
increases. An alternative hypothesis to an optimal recovery at 500 mM NaCl may
therefore be that the recovery in actual fact plateaus between 500-750 mM NaCl,
especially as pH increases from 7 to 8 (Figure 5.4.B).
As expected, the recovery of DNA in the elution fraction from the CIM SO3 increases
as the NaCl concentration and pH are increased. This supports the conclusions
generated from the linear gradient elution data from CIM SO3 in the previous section.
DNA is eluted from the column at approximately 750 mM NaCl while virus elutes at
between 150 and 350 mM NaCl.
155
The trade-off in terms of DNA removal is that a relatively high salt concentration of or
around 500 mM NaCl is required to recover between 68% and 84% of the infectious
virus from the column, which leads to a relatively low 80% removal of DNA relative to
the load. To confirm this, a lower NaCl concentration of 300 mM NaCl was tested at pH
7. The results showed that 92% of the DNA was removed but the recovery of infectious
virus was only 35 % resulting in a DNA purification factor of 4. A summary of this data
is tabulated in Table 5.2 and shows the effects of decreasing NaCl concentration on
percentage virus recovery and DNA removal. Some effect due to increasing pH can also
be seen to increase virus recovery and reduce DNA removal.
156
Table 5.2 Overview of the data generated from CIM SO3 DoE showing effects of
decreasing NaCl concentration on recovery and DNA removal and therefore purification
factor. Data in bold represents the conformation run described above designed to
maximise the DNA purification factor. *represents averaged data points.
Elution
condition
Percentage
virus recovery
(Elution)
Percent DNA
removal
(Elution)
DNA
purification
factor
Percent
protein
removal
(Elution)
Protein
purification
factor
750mM NaCl
pH 6
31 % 69 % 1.0 90 % 3.0
750mM NaCl
pH 8
*38 % *60 % *0.9 *81 % *2.3
500mM NaCl
pH 7
68 % 80 % 3.6 85 % 4.5
300mM NaCl
pH 7
35 % 92 % 4.3 92 % 4
250mM NaCl
pH 6
6.0 % 94 % 1.0 80 % 0.3
250mM NaCl
pH 8
30 % 90 % 3.2 81 % 3.4
In terms of protein removal, this appears to occur mostly during the adsorption step as
between approximately 60 – 90% of the protein loaded is recovered in the flow through
fraction, and so does not bind to the column. Data from Figure 5.3 suggests that the
majority of protein bound elutes at around 300 mM NaCl either co-eluting or eluting
just before the main infectious viral peak. The DoE suggests that, unless the virus is
eluted at low NaCl and low pH protein removal remains relatively constant across the
157
design space (Table 5.2). The reason the purification factor is so low when CIM SO3 is
eluted at 250 mM NaCl pH 6 is that virus recovery is only 6%. In an attempt to visualise
the removal of impurities, especially viral aggregates, transmission electron microscopy
(TEM) was used at low magnification (8000x) to visualise CIM SO3 load and elution
samples. The run analysed was eluted at 300mM NaCl at pH 7. This data is shown in
Figure 5.5. A large viral aggregate is clearly seen in Figure 5.5.A as well as other debris
and impurities. It is unclear what the origins of these impurities are. The approximate
concentration of DNA and total protein in the load for this sample was 1364 ng/mL and
337 µg/mL, respectively. The micrograph in Figure 5.5.B shows less aggregation, single
vaccinia particles can be seen homogenously dispersed across the grid. Both images are
representative of all grids analysed (n=3) and are shown at a magnification of 8000x.
158
Figure 5.5 TEM micrographs of A) CIM SO3 load annotated to show contaminating viral
aggregate and debris B) SO3 eluate annotated to show vaccinia monomer. Virus was
eluted in 50 mM sodium phosphate 300mM NaCl pH 7.
A
B
L a rg e a g g re g a te
C e ll o r v ira l d eb ris
V a c c in ia m o n o m e r
159
Another observation from the DoE was that there were more infectious viral particles
recovered in the flow through fractions than expected. The numbers were variable, but
averaged 44% (RSD = 62%). Throughout the DoE the columns were all loaded to their
measured DBC, (3x109 pfu/mL). A single batch of material was used for the initial 6
runs, and a second batch was used for the augmented and axial points. As this result was
unexpected, the DBC study on CIM SO3 run previously was repeated with the same
batch of material as used for the initial DoE, and the results were overlaid with the
original DBC curve. This can be seen in Figure 5.4.D. There is what appears to be a
significant difference in the breakthrough curves between the two runs. The binding
capacity of the initial batch of material was approximately 3x109 pfu/mL of monolith,
whilst that of the second run was three times lower, at approximately 1x109 pfu/mL.
There is a chance that this variability is due to the error in the TCID50; however, the
coefficient of variance in the assay, as previously mentioned, is only found to be
approximately 30%, which may not explain the whole discrepancy in terms of error in
C/Co or percentage breakthrough. The difference in virus concentration in the flow
through when comparing 0% and 100% breakthrough is greater than an order of
magnitude; therefore it was speculated that the difference is due to, at least in part, the
variability in the load material. Residual impurity concentrations were analysed in the
two batches, and the main difference appeared to be the DNA concentration. In the first
DBC run, the DNA concentration in the load was calculated to be approximately 1,000
ng/mL, while the second batch had a DNA concentration of approximately 3,000
ng/mL. This observation supports the above stated hypothesis that DNA is complexed
with histone proteins resulting in competition for binding sites with the virus. Similar
effects on CIM monoliths have been cited in the literature; however, they were
explained as resulting from lipid fouling (Burden, Jin, Podgornik, & Bracewell 2012). It
is unknown why the batches vary so much in DNA concentration. One possibility is that
160
there is variation in the total number of cells present in a single batch of cells prior to
viral infection. As stated previously a single batch consists of 30x T175 flasks. Previous
work on influenza virus (Banjac, Roethl, Gelhart, Kramberger, Jarc, Jarc, Strancar,
Muster, & Peterka 2014) has also reported large variation in DNA concentration in the
virus harvest material, suggesting a resulting effect on the reproducibility of virus yield.
As mentioned elsewhere, this combination of variability in feedstock and assay
complicates process development considerably. However, this is typical of infectious
viral processes.
In summary, the difference in the binding capacities (Figure 5.4.D) explains the
increased recovery in the flow through, and therefore the overall lower recovery in the
elution fractions observed. The trade-off between the recovery of infectious virus, and
the recovery of DNA in the elution appears to limit the purification factor achievable in
CIM SO3 monoliths under the conditions tested to around 4. Interestingly, even when
the load was reduced to 1x109 pfu/mL, the purification factor was not improved.
5.5 Evaluation of CIM SO3 as a suitable capture step for vaccinia
virus
CIM SO3 does appear to be able to resolve most of the infectious viral particles from
the contaminating DNA and protein in the load. It is not, however, able to remove
enough DNA to achieve the required specifications for DNA removal specified by the
WHO. It is assumed that this will remain at 10ng DNA/dose (World Health
Organization 1998), and that vaccinia will be required at a dose of approximately 1x109
pfu/dose.
161
5.5.1 Evaluation into the suitability of Benzonase® as a pre-
chromatographic treatment step.
Benzonase® produced by (Merk KGaA, Darmstadt, Germany) is a CGMP compliant
genetically engineered endonuclease, designed to digest DNA from biological feed
streams. The disadvantage of using Benzonase® is that it is expensive, and its removal
has to be validated. It is, however, the only effective biochemical method of removing
DNA and RNA that is licenced for manufacturing use.
In order to evaluate the suitability of Benzonase® for the use in the manufacture of
vaccinia virus, an experimental protocol was designed to test different pre-treatment
conditions with and without Benzonase® prior to CIM SO3 capture. The rationale here
was to evaluate the effectiveness of different nuclease treatment schedules at removing
DNA contamination. The idea was that this data could then be compared with an
assumed process relying on two orthogonal monolith steps but without Benzonase®.
The data in Table 5.3 shows the pre-treatment conditions used in each experiment. DNA
purification factors are given, along with percentage DNA removal and percentage
infectious virus recovery. The mass of DNA per dose in the elution is also shown,
assuming a required dose of 1x109
pfu (Heo, Reid, Ruo, Breitbach, Rose, Bloomston,
Cho, Lim, Chung, Kim, Burke, Lencioni, Hickman, Moon, Lee, Kim, Daneshmand,
Dubois, Longpre, Ngo, Rooney, Bell, Rhee, Patt, Hwang, & Kirn 2013), along with the
number of uses each monolith has had including the experiment in question. Monoliths
were loaded to a capacity of 1x109 pfu/mL (5 mL) and eluted using a stepwise elution
schedule in 50mM sodium phosphate 500mM NaCl pH 7 as shown in Figure 3.3. The
same batch of virus was used in all experiment. DNA concentration in the harvest
control runs showed variability of less than 10% (RSD 9.75%).
162
Table 5.3 Benzonase study showing experimental conditions including incubation time and
temperature, DNA purification factor (PF) % DNA removal in the elution fraction,
measured DNA per dose, % recovery of infectious virus, and number column reuses are
shown. Concentration of Benzonase used in all experiments was 150 U/mL. Harvest
material was all from same batch and thawed at 37 ⁰C. No additional Mg2+
Ions were
added to harvest material prior to treatment with Benzonase. DMEM contains 0.81mM
Magnesium Sulfate (MgS04). RT = Room Temperature. Control = Run performed without
Benzonase®
Conditions Total
DNA
Harvest
(ng)
(post-
treatment)
DNA PF
Elution
% DNA
Removal
Elution
DNA/dose
Elution
(1x109
pfu)
%
Infectious
Virus
Recovery
Elution
Number
of
Reuses
Benzonase
®
1 hr RT
9703 1.56 88 5399 19 6
Control
1 hr RT 19614 0.98 86 8442 14 8
Benzonase
®
6 hr 4-8 ⁰C
8525 1.83 87 3537 24 3
Control
6 hr 4-8 ⁰C 23857 0.96 93 13383 6 5
Control
New
column,
No
incubation
21781 2.83 70 3974 84 1
What is immediately obvious when analysing the data in Table 5.3 is that Benzonase®
does not remove the entire mass of DNA present in feed stream. It in fact removes
approximately half of it. This phenomenon has previously been noted in the literature
by other groups working with vaccinia virus (Jordan, Weimer, Iarusso, Bernhardt, Lohr,
& Sandig 2015b), and has been suggested to be the result of complexes formed between
the virus and contaminating DNA molecules. The data in this thesis shows that vaccinia
forms large aggregates, especially in high salt conditions. It is perhaps reasonable to
speculate, therefore, that Benzonase® would be unable to get close enough to the DNA,
163
especially if located towards the centre of a large aggregate molecule, to be able to
hydrolyse it.
5.5.2 Evaluation of CIM SO3 robustness
The data in Table 5.3 also shows a reduced recovery of infectious virus from CIM SO3
when eluted at 500 mM NaCl when compared to data generated when optimising the
stepwise elution (Figure 5.4). It was originally thought that this could be the result of
variation in DNA concentration in the load, however, there does not appear to be a trend
when analysing the data in Table 5.3. As previously stated the RSD between the control
runs in terms of DNA concentration was 9.75 %. The optimisation runs, as well as the
Benzonase® control run, were performed on new columns suggesting that a residual
foulant may have built up during the column lifetime. It was speculated that this may
have changed the surface chemistry of the monolith sufficiently to affect the retention of
virus on the column. As it is difficult to analyse the foulant layer itself, an experiment
was designed with a column that had already been used twice, in an attempt to repeat
the low recovery of infectious virus and then to regenerate the monolith performance
using a variety of CIP strategies. The results can be seen in Table 5.4.
164
Table 5.4 experimental CIP buffers used in an attempt to regenerate a CIM SO3
monoliths. Number of particles loaded, elution buffer conditions and batch of material
used for each run was constant. Percentage infectious virus in the flow through fraction
was less than 1% for each run. Note elution peak was collected from 100-100 mAu
(Optical Path length 0.2 cm) CIP conditions shown for each run were performed on the
run before, as so where the last CIP conditions seen by the column prior to loading.
means followed by.
CIP conditions
performed on
previous run
Infectious virus
% Recovery in
500 mM NaCl
Infectious virus
% Recovery in
2 M NaCl
Number
of reuses
Run 1
Normal CIP
1M NaOH 2M NaCl
1hr incubation
27 34 3
Run 2
Experimental
CIP
80% IPA
15 min incubation 21 6 4
Run 3
Experimental
CIP
Trypsin (5 min inc.)
2M NaCl 1M
NaOH (1hr inc.)
16 14 5
Run 4
Normal CIP
1M NaOH 2M NaCl
1hr incubation
27 19 6
It should be noted that the elution peak was collected from 100 mAu on its leading edge
to 100 mAu on its tailing edge rather than using a 10 mL fixed volume elution as
mentioned previously. This variance in the method does not appear to have affected the
results. The recovery data matches that observed during the Benzonase® experiments.
The recovery of infectious virus was lower when eluted at 500 mM NaCl than had been
observed with fresh columns during the optimisation runs. The use of standard CIP
conditions recommended by BIA Separations, 1M NaOH 2M NaCl, as well as 80%
165
IPA, and Trypsin do not appear to reverse this effect. What is interesting is that when
the columns were further washed with 50 mM sodium phosphate 2 M sodium chloride
pH 7 up to 34% of the infectious virus loaded was eluted as a separate peak; this was
only observed after BIA Separations recommended CIP buffers where used.
The data suggests that the affinity of infectious virus to the SO3 monolith may increase
when a column has been used previously. It also appears that a higher salt concentration
is required to elute the virus, suggesting that the mechanism of adsorption is still
electrostatic.
Without more data it is not possible to conclusively state that the columns are unable to
be reused, but this data is sufficient to warrant later costing analysis to include number
of monolith reuses into the sensitivity analysis shown in Section 7.4.2.
NTA data from run 1 (Table 5.4) is shown in Figure 5.6. 5 mL of virus (1x109 pfu) was
loaded onto the column, the flow through was collected with the equilibration step in 15
mL, the elution was collected in 1-3 mL, and strip was collected in 5 mL. The CIM SO3
load polydispersity appears similar to previous measurements, although smaller
diameter particles appear more variable. This is likely as the measurement was taken at
a detection threshold of 5 rather than 3 in order to better analyse the larger (300 nm)
particles in solution. Detection threshold is the minimum intensity value of an image in
order for it to qualify as a particle and therefore be tracked and analysed by the
software. It is clear that the elution fraction has a significantly smaller number of
particles then the load. This was expected based on the analysis of the load and null
prep samples shown in Figure 4.7. What is surprising is the area of each 250-350 nm
peak in both the load and elution samples relative to the other peaks in the same sample.
It appears that the smaller peak in the elution fraction is larger than the smaller peak in
166
the load fraction relative to the larger species in each distribution. It also appears that
the larger, likely virus containing peak in the elution fraction has shifted to the left by
approximately 10-20 nm. If it can be assumed that the smaller species in the load is an
impurity, which is a reasonable assumption being that it is half the size of the virus, then
this data would suggest that the purification factor of this impurity would be less than 1.
In conclusion, the elution fraction appears to contain a relatively large population of
particles of approximately 150 nm. It is not clear what this population consists of,
although either a protein DNA aggregate or degrading virus particles seem the most
likely. Another observation that can be made is that the number of particles in the load
outnumbers the particles in the flow through, elution and strip fractions. This suggests
that material is still bound to the column after the elution, which supports the argument
in Section 5.5.2 that proposes that a foulant is adsorbing to the column and reducing
yield after multiple injections.
Figure 5.6 NTA data showing size distribution of SO3 load, flow through, elution and strip
fractions. CIM SO3 monolith had been used 3 times; NTA data is showing mean values
from 5 tracking experiments, Data from run 1 (Table 5.4)
1 0 0 3 0 0 5 0 0 7 0 0 9 0 0
0
5 .01 0 8
1 .01 0 9
1 .51 0 9
P a r t ic le S i z e
Nu
mb
er
of P
arti
cle
s
L o a d
E lu tio n
F lo w T h ro u g h
S tr ip
167
5.6 An illustrative description of vaccinia virus adsorption on CIM
SO3 monoliths
It has been shown in this chapter that vaccinia virus is able to adsorb to CIM SO3
monoliths at 2 pH units above its isoelectric point.
Both the gradient and stepwise elution runs show that the DNA adsorbed to the
monolith elutes at a higher NaCl concentration than the majority of the infectious viral
particles. There are two hypotheses that might explain this:
Hypothesis 1 is that the DNA is complexed with a strong cationic molecule which is
able to interact with the monolith.
Hypothesis 2 is that the DNA is able to associate with large viral aggregate molecules
that have a higher affinity to the CIM SO3 stationary phase than smaller individual viral
particles.
5.6.1 Hypothesis 1
It is known that the majority of the DNA in the load does not adsorb to the column as it
was recovered in the flow through fractions collected during the CIM SO3 runs. It has
been speculated, based on SEM images taken of both the DNA in the load and the flow
through, that the DNA present is complexed with particles similar in size and
morphology to histone proteins. As histone proteins are positively charged, this would
explain why DNA is able to bind to the stationary phase and may also explain why
DNA is able to form a bridge between the virus and the monolith.
168
5.6.2 Hypothesis 2
In the second hypothesis, DNA is able to associate with large viral particles. There is a
chance that a positive charge could come from the virus itself, but if this were true, and
DNA was binding to positively charged regions on the viral surface, then it might be
assumed that either the virus and DNA would co-elute, or the DNA would elute first.
The reason for this assumption is that increases in the ionic strength of the mobile phase
during the elution step would likely break the electrostatic interactions between either
the virus and DNA, eluting DNA, or the virus and the column, eluting both DNA and
virus. The fact that this does not happen suggests that the DNA is able to be retained
without the majority of the infectious virus present. For this to be the case, DNA must
either have a strong positive charge and so be able to adsorb directly to the CIM SO3 or
be associated with large viral aggregates, likely made up of infectious and non-
infectious viral particles that do not break apart when NaCl is introduced and elute later
in the gradient.
Information in the literature on histone protein interactions (Rippe et al. 2008) is
interesting. It shows that histone proteins, in the absence of DNA form aggregates
which are stabilised at high salt (2 M NaCl). As the ionic strength decreases to
physiological levels (100-150mM NaCl) the histone complexes begin to disassociate
and at low ionic strength, histone proteins become soluble and unfold. In vitro
reconstruction of nucleosomes is achieved by slowly reducing the salt concentration
from 2 M to physiological ionic strength. This allows assembly of the nucleosome
particles in an ordered manner and prevents histone-DNA aggregation. During CIEX
experiments this would not have occurred and it seam conceivable that DNA-histone
aggregates may have been formed in the harvest that were then able to interact with
viral particles or aggregates and facilitate adsorption onto CIM SO3. In addition to this,
169
previous work performed on histone purification using CIEX chromatography shows
that histone proteins bind to SP ligands at 0.2M NaCl and require between 0.8 and 2 M
NaCl to elute (Rodriguez-Collazo et al. 2009). Histone proteins are amongst the most
basic proteins known with an isoelectric point of around 11 as discussed in (Chung et al.
1978).
While more supporting experiments need to be performed, the results generated when
vaccinia was treated with Benzonase® endonuclease also support this theory.
Benzonase® is not able to degrade all of the DNA in the load suggesting that large
aggregates are present. This has been confirmed by TEM, as shown in Figure 4.10. It
has also been shown, by SEM, that DNA is able to associate with vaccinia, suggesting
that it may be responsible for the inter-particle interactions. This will be described in
more detail in Chapter 6.
An attempt at illustrating these molecules and their associated affinity to the CIM SO3
is shown in Figure 5.7. The removal of viral aggregates in the CIM SO3 elution at 300
mM NaCl supports the theory that the affinity of individual vaccinia particles for the
CIM SO3 stationary phase is lower than that of large aggregates. The increased affinity
of the DNA, potential complexed with positively charged histone proteins is supported
by the linear gradient elution runs Figure 5.3 and the DoE Figure 5.4.A-C.
170
Figure 5.7 Schematic to show proposed binding affinity of vaccinia virus and Vaccinia-
DNA complexes on CIM S03 Monoliths.
171
5.7 Conclusion
This chapter has illustrated that Vaccinia virus has some unusual properties in that it is
able to bind to both CIEX and AIEX monoliths. From the initial linear gradient elution
runs performed, CIM SO3 appears to be able to separate a large proportion of DNA
from crude vaccinia viral harvest. DNA appears to adsorb more strongly to the column
than viral particles, suggesting the presence of a strongly cationic molecule in the feed
that is able to form a bridge between the anionic stationary phase and the DNA.
When using a stepwise elution schedule, the unit operation is still able to separate virus
from DNA. However, as DNA binds more strongly than the virus a pre-elution wash
step cannot be used to remove DNA. A trade-off appears to occur in terms of the
recovery of infectious virus and subsequent removal of DNA as an increased NaCl
concentration increases the recovery of both infectious virus and DNA. Greater than 50
% recovery is achievable at NaCl concentrations around 500 mM, however 20% of the
total DNA loaded is co-eluted at this molarity. Lower salt concentrations (300 mM
NaCl) reduce the levels of eluted DNA to around 10%, but this also reduces the
recovery of infectious virus to around 35%. The resulting DNA purification factor
appears to remain at around 4. According to TEM data, 300mM NaCl is able to remove
large viral aggregates present in the CIM SO3 load. This data is qualitative rather than
quantitative but is useful of note especially if levels of virus aggregation are defined as a
critical qualities attribute. CIM SO3 monoliths appear to require more NaCl to elute
vaccinia after they have been used more than once. This potentially supports the
hypothesis that histone driven DNA – viral aggregation occurs that increases the affinity
of virus adsorption onto the SO3 ligand. With this in mind, the most efficient
application of CIM SO3 is likely to be as a single use capture step, vaccinia would be
loaded at low salt and eluted at around 500 mM NaCl in order to maximise recovery
172
and remove the majority of contaminating DNA. A second step would then need to be
performed to remove the residual DNA and protein to levels acceptable for clinical
application.
Benzonase® is able to reduce the levels of DNA in the load, but not by enough to
remove all contaminating DNA without the need for another step. This conclusion is
supported by previous work on vaccinia virus.
At this stage in process development, it was concluded that no further studies would be
done with CIM SO3 monoliths until other systems were considered. There are some
theoretical advantages of using a HIC system, and as vaccinia is shown to be reasonably
stable in high salt, in terms of infectivity, CIM OH monoliths were evaluated next.
173
Chapter 6
Hydrophobic Interaction
Chromatography Process Development
6.1 Introduction
CIM OH monoliths are produced by the hydrolysis of epoxy ligands on the
polymethacrylate monolith surface to produce a high density of hydroxyl groups. This
makes CIM OH monoliths hydrophilic and negatively charged. The high salt levels
usually needed in hydrophobic interaction chromatography (HIC) can damage labile
viruses. Nevertheless standard HIC resins with phenyl and butyl groups have been used
previously in both virus and VLP capture and intermediate polishing steps (Burden, Jin,
Podgornik, & Bracewell 2012;Jordan, Weimer, Iarusso, Bernhardt, Lohr, & Sandig
2015b;Wolff, Siewert, Hansen, Faber, & Reichl 2010a). It is more common today to use
ion exchange (IEX) monoliths to purify viruses, (Banjac, Roethl, Gelhart, Kramberger,
Jarc, Jarc, Strancar, Muster, & Peterka 2014;Gerster, Kopecky, Hammerschmidt,
Klausberger, Krammer, Grabherr, Mersich, Urbas, Kramberger, Paril, Schreiner,
Nobauer, Razzazi-Fazeli, & Jungbauer 2013;Kramberger, Petrovic, Strancar, &
Ravnikar 2004;Kramberger, Peterka, Boben, Ravnikar, & Strancar 2007;Kramberger,
Honour, Herman, Smrekar, & Peterka 2010;Rupar, Ravnikar, Tusek-Znidaric,
Kramberger, Glais, & Gutierrez-Aguirre 2013) although the low hydrophobicity of CIM
OH coupled with the relatively high salt tolerance of vaccinia virus (Figure 4.5) may
reduce or minimise losses in infectivity. CIM OH could also be able to offer unique
174
selectivity, especially for DNA clearance, and is therefore considered here for vaccinia
purification.
6.2 CIM OH monolith screening study
In order to evaluate the suitability of CIM OH monoliths as an initial capture and
purification step for vaccinia virus, a number of decisions needed to be made from the
outset, prior to the generation of any data, and based on previous experience and
information in the literature. It was decided to start by running initial bind-and-elute
experiments at neutral or weakly basic pH in sodium phosphate buffer. The reason for
this was that it was speculated that a lower pH would reduce the stability of vaccinia
virus as well as potentially increase the binding affinity of the virus to the monolith and
so reduce the recovery in the elution. The mobile phase modulator used to drive
adsorption was ammonium sulfate due to its high ionic strength and kosmotropic nature.
1 mL monoliths were used in all experiments. Chromatography run setup is shown in
Figure 3.3. A process flow diagram detailing how vaccinia was produced can be seen is
shown in Figure 3.1.
Three experiments were performed using the following loading condition: vaccinia
harvest was diluted in 50 mM sodium phosphate pH 7 in both 1 and 2 M ammonium
sulfate, and pH 8.5 in 2 M ammonium sulfate. Rather than initially optimising a
diafiltration step, vaccinia was diluted 1:5 with each chosen load buffer in order to bring
the pH and conductivity close to that of the buffer used. Table 6.1 shows the recorded
conductivity and pH for each buffer and diluted load fraction. As the binding capacity
of CIM OH was unknown at this point, a binding capacity of ≤ 1x1010
pfu of virus was
assumed, and so 1x1010
pfu of viral harvest material was diluted in load buffer, filtered,
175
and loaded into each column. Viral harvest, chromatography load, flow through and
elution fractions were collected in order to generate a mass balance. The columns were
eluted at the same pH as the load, but without ammonium sulfate in the buffer. The strip
buffer consisted of pure water and the column was cleaned with 1 M NaOH 2 M NaCl.
Table 6.1 Equilibration buffer and diluted viral load material conductivity and pH
conditions. Conductivity meter was uncalibrated, so it was used as a reference only. The
Chromatography system was not equipped with a pH meter. (N/T- not tested). AS stands
for ammonium sulfate.
Equilibration buffer / Load
conditions
Conductivity (mS/cm) pH
1 M AS pH 7 Buffer 143.17 7.00 +/- 0.09
1 M AS pH 7 Load 122.70 N/T
2 M AS pH 7 Buffer 231.52 7.00 +/- 0.09
2 M AS pH 7 Load 205.78 N/T
2 M AS pH 8.5 Buffer N/T 8.5 +/- 0.09
2 M AS pH 8.5 Load 200.40 N/T
The results of the screening study are presented in Figure 6.1.A and show the recovery
of infectious virus in the flow through and elution fractions, as well as the calculated
recovery assuming no loss in the flow through fraction. This additional analysis was
performed in order to illustrate the total number of infectious particles unaccounted for,
as infectious particle balances rarely close. The best explanation for why this occurs is
that infectious particles are either inactivated during the unit operation, or remain on the
column after the elution and strip steps and are likely hydrolysed when washed with
NaOH during the CIP step. The data appears to show that in 1 M ammonium sulfate the
176
virus is unable to bind strongly to the column, because around 60% of the infectious
particles loaded are recovered in the flow through fraction. As a result, only around 40%
of particles adsorbed are recovered in the elution. It should be noted that the relative
standard deviation (RSD) in the infectivity assay is approximately +/- 30%.
177
OH
1M
AS
pH
7
OH
2M
AS
pH
7
OH
2M
AS
pH
8.5
0
2 .01 0 0 9
4 .01 0 0 9
6 .01 0 0 9
8 .01 0 0 9
1 .01 0 1 0
C h e m is try , [S a lt] lo a d , p H
Infe
cti
ou
s V
ira
l P
art
icle
s (
pfu
)
N u m b e r o f P a r t ic le s L o a d e d o n to th e H IC C o lu m n
C a p a c ity A c h ie v e d
B
OH
1M
AS
pH
7
OH
2M
AS
pH
7
OH
2M
AS
pH
8.5
0
2 0
4 0
6 0
8 0
1 0 0
C h e m is try , [S a lt] lo a d , p H
Infe
cti
ou
s V
iru
s R
ec
ov
ery
(%
)
I n fe c t io u s V iru s R e c o v e ry F lo w T h ro u g h (% )
In fe c t io u s V iru s R e c o v e ry E lu t io n (% )
C a lc u la te d In fe c t io u s R e c o v e ry A s s u m in g N o L o s s in F lo w T h ro u g h
A
Figure 6.1 A) Histogram showing vaccinia virus loaded in 1 and 2 M ammonium sulfate at
pH 7 and 2M ammonium sulfate at pH 8.5. Red bars show the percentage of infections
virus in the flow through fraction relative to the load, blue bars shows percentage of
infectious virus in the elution fraction relative to the load, and black bars show the
percentage infectious virus in the elution minus the total number of particles lost in the
flow through. B) Histogram showing the number of viral particles loaded vs the capacity
achieved on CIM OH.
178
In 2 M ammonium sulfate virus is present in very low amounts in the flow through with
approximately 50-80% recovered in the elution fraction. At first glance this appears
promising, however, after a more careful look at the data it was discovered that the virus
recovery across the 0.8µm pre-filter when loaded in 2M ammonium sulfate was only
6%. This is graphically represented in Figure 6.1.B and is shown in Table 6.2. The
number of virus particles in the harvest (pre filtration) and load fractions are shown for
each of the three runs along with the estimated capacities achieved on CIM OH along
with the estimated percentage recovery achieved across the pre-filter and in the CIM
OH elution. It can be seen that the number of particles loaded when the experiment was
run at 1 M ammonium sulfate is significantly higher than when run at 2 M ammonium
sulfate. It also shows a higher capacity achieved when run at 1 M ammonium sulfate.
The low capacity at 2 M ammonium sulfate is thought to be the result of the low load
challenge due to the low recovery of virus across the pre-filter. The higher recovery
across the pre-filter when run at 1 M ammonium sulfate explains the higher load
challenge on the column. The increased number of infectious virus particles in the flow
through for this run suggests that the column may have been overloaded.
Chromatograms for runs with 1 M and 2 M ammonium sulfate pH 7 are shown in
Figure 6.2.
179
0 2 0 4 0 6 0 8 0 1 0 0
0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
C o lu m n V o lu m e s
mA
u
(28
0 n
m)
Co
nd
uc
tivity
(mS
/cm
)
A b s o rb a n c e (2 8 0 n m )
C o n d u c t iv i ty (m S /c m )
0 2 0 4 0 6 0 8 0
0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
0
5 0
1 0 0
1 5 0
C o lu m n V o lu m e s
mA
u
(28
0 n
m)
Co
nd
uc
tivity
(mS
/cm
)
A b s o rb a n c e (2 8 0 n m )
C o n d u c t iv i ty (m S /c m )
A
B
L o a d
E lu t io nW a s h
L o a d
W a s h E lu t io n
Figure 6.2 A) Chromatogram for 1 M AS pH 7 run B) Chromatogram for 2 M AS pH 7
180
Table 6.2 Summary of CIM OH screening study. The number of virus particles in the harvest and post 0.8µm filtration is shown along with percentage
recovery of virus across the filter and the column. Capacity achieved is calculated by number of particles in the load minus the number of particles in the
flow through. (* = Indicates that a new batch of material with a lower tire was used for 2 M AS run at pH 8.5). N/T = not tested.
Load
conditions
Number of
viral particles
in harvest
(pre 0.8 µm
filter)
(pfu)
Percentage
recovery
across
0.8 µm filter
Number of
virus particles
loaded
(post
0.8 µm filter)
(pfu)
Capacity
achieved
(pfu/mL)
Number of
viral particles
in elution
(pfu)
Recovery
in elution
relative to
harvest
Recovery
in elution
relative to
load
Recovery
in elution
relative to
capacity
achieved
DNA
purification
factor
Protein
purification
factor
1M AS
pH 7 2.48x10
10 36% 8.94x10
9 3.51x10
9 1.53x10
9 6% 17% 44% 3.71 3.00
2M AS
pH 7 1.56x10
10 7.0% 1.08x10
9 1.04x10
9 5.52x10
9 4% 51% 53% 1.72 2.35
*2M AS
pH 8.5 2.09x10
9 21% 4.38x10
8 4.35x10
8 3.43x10
9 16% 78% 79% 3.31 N/T
181
The elution buffer conditions tested during the screening experiments were not designed
to test the ability of CIM OH to remove DNA or total protein, but to examine whether
virus was able to bind and be eluted from the column without losing infectivity.
Interestingly however, DNA and total protein purification factors calculated during
these runs show promising results, as shown in Table 6.2. This is comparable to
purification factors achieved for CIM SO3 after elution step optimisation. CIM SO3
achieves a DNA and protein purification factor of approximately 4 when eluted in 300
mM NaCl. The majority of DNA and contaminating protein is not able to bind to CIM
OH. This explains why relatively high purification factors are achievable without
elution buffer optimisation.
6.3 OH load buffer optimisation
In order to evaluate HIC as a potential capture and polishing step for vaccinia virus, an
experiment had to be designed to address pre-column filterability.
It was hypothesised that the low recovery across the 0.8µm filter was a result of viral
aggregation in the presence of kosmotropic salts in the HIC load buffer. Experiments
were designed in order to confirm this, and were aimed at increasing the recovery across
the filter by modifying the load buffer conditions.
The filters used in all studies were surfactant free cellulose acetate (SFCA) 0.8µm
syringe filters manufactured by Sartorius (Gottingen, Germany). Cellulose acetate
membranes are hydrophilic, and, in theory, ideally suited for pressure driven filtration
due to low protein adsorption properties.
182
In order to increase filterability, a study was designed to test the effects of ammonium
sulfate and NaCl concentration on both the pre-column filterability of the virus, and its
ability to then adsorb efficiently to CIM OH. The conditions tested are shown in (Table
6.3).
Table 6.3 CIM OH load buffer optimisation study showing different NaCl and (NH4)2SO4
concentrations tested. Run 1 (bold) was taken from screening experiments.
Run [NaCl] (M) [(NH4)2SO4] (M)
1 0 2
2 1 1.5
3 2 1
4 3 0.5
5 4 0
Ammonium sulfate concentration was reduced by 0.5 M per run (from 1.5 M to 0 M),
while the sodium chloride concentration was increased by 1 M per run (from 1 M to 4
M). A volume of 2.3 mL of virus with a titre of approximately 1x109 pfu/mL was
diluted 1:5 in load buffer and was loaded onto the (filter-column) units as described in
Table 6.3. The results, as can be seen in Figure 6.3.A, show the recovery of infectious
virus in the 0.8µm filtrate relative to the harvest, and the flow through and elution
fractions relative to the load.
Figure 6.3.B shows the DNA and total protein purification factors achieved in the
elution fractions for each run. Table 6.4 also shows a summary of this data set and
183
includes the number of infectious particles recovered pre and post filtration as well as in
the flow through and elution fractions. The data suggests that as the ammonium sulfate
is reduced, the filterability of the material increases. This is evident by the increase in
recovery across the 0.8µm filter. The recovery when loaded in 4 M NaCl, without
ammonium sulfate, and when loaded without any salt (shown in Figure 6.4) are similar.
This suggests that NaCl does not strongly affect filterability.
OH
1M
AS
OH
2M
AS
OH
1.5
M A
S 1
M N
aC
l
OH
1M
AS
2M
Na
Cl
OH
0.5
M A
S 3
M N
aC
l
OH
4M
Na
Cl0
5 0
1 0 0
1 5 0
C h e m is try , [S a lt] lo a d
Re
co
ve
ry (
%)
0 .8 m F ilte r
F lo w T h ro u g h
E lu t io n
OH
1M
AS
OH
2M
AS
OH
1.5
M A
S 1
M N
aC
l
OH
1M
AS
2M
Na
Cl
OH
0.5
M A
S 3
M N
aC
l
OH
4M
Na
Cl0
5
1 0
1 5
C h e m is try , [S a lt] lo a d
Pu
rif
ca
tio
n f
ac
tor
D N A
P r o te in
A B
Figure 6.3 A) CIM OH load optimisation study showing the percentage recovery of
infectious virus across the 0.8µm filtration step relative to the harvest, and the recovery of
infectious virus in the flow though and elution fractions relative to the load. B)
Purification factors achieved for DNA and total protein. Elution buffers used for all
experiments were 50mM sodium phosphate pH 7. ~2x109 pfu of vaccinia was filtered, and
all virus in the filtrate was loaded onto the columns. (AS = Ammonium sulfate).
184
Table 6.4 CIM OH buffer optimisation study showing the number of infectious virus particles present before and after dilution and 0.8 µm filtration, in the
flow through and elution fractions. The percentage recovery of infections virus in the flow through and elution fractions, and the DNA and protein
purification factors in the elution fractions for each condition tested. Rows in bold represent original data from screening study (different batch of virus,
higher load volume (10mL). Buffer optimisation study came from a single batch of virus, 2.3 mL virus was diluted and loaded onto filter-column system.
Conditions Number of
particles
(Harvest)
Number of
particles
(Post 0.8 µm
Filter)
Number of
particles
(Flow
Through)
Number of
particles
(Elution)
Percentage
recovery
(Flow
Through)
Percentage
recovery
(Elution)
DNA
purification
factor
(Elution)
Protein
purification
factor
(Elution)
1 M AS pH 7 2.48x1010
8.94x109 5.43x10
9 1.53x10
9 61.0 20.0 3.71 3.01
2 M AS pH 7 1.56x1010
1.08x109 4.38x10
7 5.52x10
8 4.00 51.0 0.05 2.35
1.5 M AS
1 M NaCl pH 7 4.80x10
9 9.63x10
8 1.93x10
7 1.46x10
8 2.00 15.0 2.82 3.06
1 M AS
2 M NaCl pH 7 4.58x10
9 1.53x10
9 6.59x10
7 1.22x10
9 4.00 80.0 6.62 10.6
0.5 M AS
3 M NaCl pH 7 3.46x10
9 2.56x10
9 2.75x10
9 9.62x10
8 108 38.0 5.74 7.84
0.0 M AS
4 M NaCl pH 7 3.78x10
9 2.36x10
9 3.19x10
9 4.60x10
8 135 19.0 3.42 5.85
185
The trade-off is that as the ammonium sulfate concentration decreases, the adsorption
efficiency of virus onto the CIM OH also decreases. This is shown by an increase in
recovery in the flow through fraction, which is dramatic for ammonium sulfate
concentrations of 0.5 M and below. The same relationship is also evident in the DNA
and total protein purification factors achieved in the elution fractions from each run.
This is the case as purification factor is a function of both viral recovery and
DNA/protein removal as described in Equation 3-7. It can be seen that the total particle
balance is greater than 100% when low concentrations of ammonium sulfate are used in
the load buffer. This is most likely due to the variability in the analytics.
Based on the data in Figure 6.3 it was concluded that the best load buffer condition
tested was 50 mM sodium phosphate 2 M sodium chloride 1 M ammonium sulfate pH
7. This will from now on be referred to as optimised HIC load buffer.
In order to test the robustness of this conclusion, the percentage recovery of virus across
the filter in different load buffers was tested with and without salt, and in optimised HIC
load buffer as shown in Figure 6.4. Box and whisker plots are shown together with the
raw data values for each condition tested. The box represents the interquartile range
with the median shown in the centre. The whiskers show the range.
The plot illustrates that when vaccinia is filtered in optimised HIC load buffer a
reasonably robust recovery of infectious virus is achieved. This recovery is
approximately 35%. The recovery when loaded with 2 M ammonium sulfate was
unacceptably low at around 6%. Interestingly, when run without salt the filtration
recovery is higher, but also more variable. This may be due to more dynamic
aggregation patterns without the presence of ammonium sulfate as a mobile phase
186
modulator acting to promote aggregation. This would likely affect the TCID50 as the
number of particles per unit volume would likely be more stable.
Figure 6.4 also shows the filtration recovery of virus in 2 M ammonium sulfate at pH
8.5 after high g centrifugation. All other fractions were centrifuged at a relative
centrifugal force (RCF) of 440, however this sample was centrifuged at 771 to test
whether removing more debris prior to dilution in HIC load buffer would prevent
filtration fouling. This experiment was only performed once, and the data showed no
change in the infectious viral recovery.
In future work, an evaluation of alternative filters would be recommended as described
in Section 9.2. This work was focused on the development of a chromatographic
separation, so while not perfect, a median recovery of 35% was taken as an acceptable
pre-filtration recovery and the optimised HIC load buffer conditions were taken forward
for the development of a capture step for vaccinia virus.
187
Sodiu
m P
hosp
ha te
wit
hout S
a lt p
H 7
(A
)
2 M
AS
wit
hout N
aCl pH
8.5
(B
)
1M
AS
wit
hout N
aCl pH
7 (
C)
2 M
AS
wit
hout N
aCl pH
8.5
af t
e r hig
h g
ce n
trif
uga ti
on (
D)
Opti
mis
e d H
IC loa d
buf f
e r (E
)
0
5 0
1 0 0
1 5 0
C o n d i t io n s
Re
co
ve
ry o
f in
fec
tio
us
vir
us
(%
)
Figure 6.4 Replicate data showing filterability expressed as percentage recovery of virus
across the filter in different load buffers. (High g centrifugation was attempted to remove
cell debris at an RCF of ~771 for 5 min; normal centrifugation conditions consisted of an
RCF of ~440 for 5 min) (AS = Ammonium sulfate)
188
6.4 Evaluation of dynamic binding capacity
Binding capacity studies were performed in optimised HIC load buffer at pH 7 and pH 8
as shown in Figure 6.5. The load material at pH 7 was calculated to be 1.42x108 pfu/mL
and at pH 8 to be 1.66x108 pfu/mL. While the exact breakthrough curve is hard to plot
with certainty due to the error in the TCID50, it is clear that initial breakthrough occurs
at approximately the same time in each experiment, corresponding to 1x109
pfu/mL of
monolith. The slope of the breakthrough curve appears to be fairly shallow suggesting
that adsorption is relatively inefficient. Further work needs to be carried out to
understand the exact mechanism of adsorption; however, the data in Figure 6.9 suggests
that aggregation in the presence of ammonium sulfate may be reversible, dynamic and
variable depending on the diluent used. It is likely that the relative affinity of viral
aggregates to the CIM OH stationary phase is dependent on the hydrodynamic diameter
of the particles. If the aggregation profile and therefore the average particle diameters
keeps changing, then this could lead to different adsorption kinetics for each aggregate
particle.
189
1 .01 0 9 3 .01 0 9 5 .01 0 9
0
2 0
4 0
6 0
8 0
1 0 0
D B C (p fu /m L )
% (
C/C
o)
p H 7
p H 8
Figure 6.5 CIM OH DBC run in optimised HIC load buffer at pH 7 and pH 8. Error bars
show error in TCID50 data and are generated from 1 DBC study at each pH measured in
triplicate, error bars show 1 SD. Y-error bars are shown as shaded areas and X-error bars
are shown as horizontal lines.
6.5 CIM OH step wise elution design
A linear gradient elution was run over 25 CVs and fractionated in order to evaluate
when infectious virus particles of 250-350 nm, DNA and total protein eluted from the
column. Buffer A consisted of optimised HIC load buffer and buffer B was the same
elution buffer as used in the early screening studies, 50mM sodium phosphate pH 7.
The results are shown in Figure 6.6.A. DNA appears to elute first after around 10 CVs,
followed by the virus towards the end of the gradient. This data suggested that it might
be possible to remove DNA in a pre-elution wash step by decreasing the salt
concentration enough to elute the DNA without eluting the virus. In order to test this
hypothesis and make the process more commercially viable a stepwise elution schedule
190
needed to be developed. Two runs were therefore performed, the first at pH 7 and the
second at pH 8; virus was eluted over 25 column volumes using a multi-step elution
schedule. This consisted of reducing the ammonium sulfate and NaCl concentrations by
20% every 5 column volumes by increasing the percentage of buffer B. Each of the
elution steps is shown in Table 6.5.
Table 6.5 CIM OH Multi-Stepwise elution schedule design showing percentage buffer A
and percentage buffer B for each step (Each step was 5 CVs) Row in bold (step 4) shows
buffer molarity in which the majority of infectious virus was eluted.
Step Percentage buffer A
Equilibration buffer
(Optimised HIC load
buffer pH 7-8)
Percentage buffer B
Elution buffer
(50 mM sodium
phosphate pH 7-8)
Approximate molarity of
elution fraction
1 80 20 0.8 M AS 1.6 M NaCl
2 60 40 0.6 M AS 1.2 M NaCl
3 40 60 0.4 M AS 0.8 M NaCl
4 20 80 0.2 M AS 0.4 M NaCl
5 0 100 0.0 M AS 0.0 M NaCl
191
The corresponding chromatograms are shown in Figure 6.6 B and C. Virus in each
elution pool was analysed using TCID50 and NTA. The chromatograms show that both
measurements agree in terms of where the virus is being eluted from the columns,
although as expected, the NTA overestimates the total number of virus particles in each
fraction.
Figure 6.6C shows that loading at pH 8 gives a slightly better removal of DNA than at
pH 7 therefore the 3rd
and 4th
steps when run at pH 7 were increased from 5 CVs to 10
CVs in an attempt to maximise DNA removal in the 3rd
fraction and maximise virus
yield in the 4th
. The resulting chromatogram is shown in Figure 6.6D. It appears when
analysing the chromatogram that this worked, however, the raw data in Table 6.6 shows
that a lower overall removal of DNA relative to the load was achieved. This is due to a
higher concentration of DNA in the starting material when compared to the initial
experiments run at pH 7. All CIM OH runs where performed twice.
192
6 8 10 12 14 16 18 20 22 24 26 28 300
50
100
150
200
250
300
DN
A (
ng
), A
28
0 (
nm
), C
on
d (
mS
/cm
) Cond (mS/cm)
A280 (nm)
DNA (ng)
Infectious Viral Particles (pfu)
Particles (250-350nm)
Column Volumes
A
0.0E+00
5.0E+06
1.0E+07
1.5E+07
Infe
ctio
us V
ira
l P
art
icle
s (
pfu
)
0.00E+000
5.00E+008
1.00E+009
1.50E+009
2.00E+009
Pa
rtic
les (
25
0-3
50
nm
)
2 4 6 8 10
Time (min)
0
50
100
150
200
250
300
DNA (ng), A280 (nm), Cond (mS/cm)
DN
A (
ng
), A
280
(n
m),
Co
nd
(m
S/c
m)
0.0E+00
5.0E+09
1.0E+10
1.5E+10
2.0E+10
Particles (250-350nm)
Pa
rtic
les (
25
0-3
50
nm
)
0.0E+00
4.4E+07
8.8E+07
1.3E+08
1.8E+08
2.2E+08
Infectious Virus (pfu)
Infe
ctio
us V
iru
s (
pfu
) A280 (nm) Cond (mS/cm)
B
2 4 6 8 10500
1000
1500
2000
2500
DN
A (
ng)
Time (min)
DNA (ng)
Particles (250-350nm)
Infectious Viral Particles (pfu)
A280 (nm) Cond (mS/cm)
Cond (mS/cm)
C
0.0E+00
1.0E+10
2.0E+10
Part
icle
s (
250-3
50nm
)
0.0E+00
5.0E+08
1.0E+09
1.5E+09
Infe
ctious V
iral P
art
icle
s (
pfu
)
0
50
100
150
200
250
300
A280 (
nm
) C
ond (
mS
/cm
)
193
Figure 6.6 CIM OH Linear and stepwise elution schedules A) Linear gradient elution over
25 CVs B) multi-stepwise elution schedule run at pH 7 C) multi-stepwise elution schedule
run at pH 8 (DNA and particle data only collected across elution peak 3 rather then 5 data
points plotted) D) multi-stepwise elution schedule run with additional column volumes for
3rd
and 4th
steps pH 7.
Data in Table 6.6 shows that the total protein concentrations in all fractions were below
the limits of quantification. This was the case when analysed with both BCA and micro
BCA protein assays. It is understood that micro BCA, while having an LLOQ of 2
µg/mL, is incompatible with ammonium sulate. The regular BCA assay has an LLOQ of
20 µg/mL, so for this reason an SDS page gel, shown in Figure 6.7, was run in order to
visualise the protein present in the elution. After staining with SimpleBlueTM
SafeStain
(Life Technologies, Carlsbad, CA, USA) there were no visible bands, therefore the gel
shown was silver stained. 10 µL of each fraction under non-reducing conditions was
loaded into each well. The gel shows that protein is present in the elution fractions at pH
7 and pH 8, however, at very low concentration. The majority of protein is collected in
the flow through fraction. A null prep sample, which was filtered cell lysate without
virus, was run as a control (lane 10). This appears to have a similar protein profile to the
load material, suggesting that the majority of protein in the sample was contamination
from the host cell.
2 4 6 8 10 120
500
1000
1500
2000
2500
DN
A (
ng
)
Time (min)
DNA (ng)
Particles (250-350nm)
Infectious Viral Particles (pfu)
A280, Cond (mS/cm)
Cond (mS/cm)
D
0.0E+00
2.6E+09
5.2E+09
7.8E+09
1.0E+10
1.3E+10
Pa
rtic
les (
25
0-3
50
nm
)
0.0E+00
2.2E+07
4.4E+07
6.6E+07
8.8E+07
1.1E+08
Infe
ctio
us V
ira
l P
art
icle
s (
pfu
)
0
50
100
150
200
250
300
350
400
450
500
A2
80
, C
on
d (
mS
/cm
)
194
Table 6.6 CIM OH multi-stepwise elution scheduals run at pH 7 and pH 8 (N/T = not tested) LLOQ = lower limit of quantification. % R = percentage
recovery
* = CIM OH run with elution fractions 3 and 4 increased from 5 to 10 CV’s.
** = CIM OH run without 1st and 2
nd elution fractions and with 3 and 4 increased from 5 to 10 CVs.
Conditions
tested
0.8 µm
Filter
(% R)
Number of particles
loaded
(pfu)
Mass of
DNA loaded
(ng)
Flow
Through
TCID50
(% R)
Elution
TCID50
(% R)
Elution
NTA
(% R)
Elution qPCR
(% R)
Elution
DNA
Removal
(%)
DNA
purificati
on factor
Elution
Protein
pH 7 Run 1 104 6.62x108 7200 0.16 92 35 N/T 97 21 LLOQ
pH 7 Run 2 38 5.35x108 6100 0.08 42 55 N/T 97 15 LLOQ
pH 7 Run 3* 39 5.07x108 23000 0.58 36 25 25 95 7 LLOQ
pH 7 Run 4** 34 1.12x109 25000 0.46 50 37 44 94 8 LLOQ
pH 8 Run 1 32 1.75x109 29000 0.42 90 39 N/T 96 22 LLOQ
pH 8 Run 2 34 1.08x108 18000 0.15 91 10 12 99 88 LLOQ
195
0 5 0 1 0 0 1 5 0
0
5 0
1 0 0
1 5 0
n o t d a ta
H a rv e s t L o a d F T E 3 E 4 E 5 W E N u ll P re p
p H 8 p H 7
Figure 6.7 Non reduced SDS page gel after silver staining. Fractions are from CIM OH
harvest, load, flow though, wash, and elution fractions run at pH 7 (Figure 6.6.B) and pH
8 (Figure 6.6.C). Null Prep fraction was prepared in the same way as chromatography
load material but did not contain virus material. FT = flow through, E=elution.
196
NTA size distribution data for load and elution fractions analysed from CIM OH runs at
pH 7 and pH 8 are shown in Figure 6.8. The load material originates from the same
source, although has been diluted 1:5 in optimised HIC load buffer at pH 7 (A) and pH
8 (B). The elution fractions from each run are less polydisperse than the load material,
and appear comparable in terms of particle size profile. The load fractions show
considerable differences which may be related to the change in pH; however, it is also
likely to be indicative of the high levels of variability seen in the starting material.
What is encouraging is that the variable polydispersity in the upstream does not appear
to affect the levels of polydispersity in the CIM OH elution fraction.
197
Figure 6.8 Size distribution of CIM OH stepwise elution schedule load and elution
fractions. A) data includes elution fractions 3, 4 and 5 pH 7 B) data show load and elution
pH 8. N=5 for all samples, curves represent mean values.
0 3 0 0 6 0 0 9 0 0
0
2 .01 0 8
4 .01 0 8
6 .01 0 8
8 .01 0 8
1 .01 0 9
P a r t ic le S i z e
(n m )
Nu
mb
er
of P
arti
cle
sL o a d
E lu tio n F ra c t io n 3
E lu tio n F ra c t io n 4
E lu tio n F ra c t io n 5
0 3 0 0 6 0 0 9 0 0
0
2 .01 0 8
4 .01 0 8
6 .01 0 8
8 .01 0 8
1 .01 0 9
P a r t ic le S i z e
(n m )
Nu
mb
er
of P
arti
cle
s
L o a d
E lu tio n F ra c t io n 4
A
B
198
6.6 Evaluation of robustness and process variability
The ability of CIM OH to remove contaminating DNA from the viral load material
varies depending on the total mass of DNA in the load. However, it is shown to be as
high as 99% resulting in a total mass of DNA/dose of less than 10 ng (depending on the
dose specified). A second chromatographic step would likely be necessary if vaccinia
was required at high dose. When compared to sulfated cellulose membrane absorbers
(SC-MA) evaluated in the literature, (Wolff, Siewert, Hansen, Faber, & Reichl
2010a;Wolff, Siewert, Lehmann, Hansen, Djurup, Faber, & Reichl 2010b) CIM OH
monoliths appear to have a lower binding capacity than SC-MA, which has shown to
adsorb up to 6.42x109 pfu of MVA per capsule. Each capsule tested was 75 cm
2. A
direct comparison is difficult to make as the cell line, vaccinia strain and analytical
measurements were all different, however, it appears that a higher percentage recovery
of infectious virus was achieved using CIM OH. The authors measured virus recovery
from SC-MA using an in-house ELISA, and achieved a recovery of 75%. The total
infectious recovery across the whole process was reported to be approximately 34%
although a pre-column filtration step was not included in the process stream and this
was after a second chromatography step. Infectious recovery from CIM OH is variable
but as high as 90%. CIM OH and SC-MA show comparable removal of total protein,
which is below the lower limit of quantification (LLOQ) of the BCA assay used in both
studies. Total DNA removal was slightly higher using CIM OH, although still
comparable to SC-MA. The best result shown for SC-MA was a removal of 98% of the
total DNA loaded while CIM OH removed up to 99%. This corresponds to an
impressive purification factor of 88.
199
The data in Table 6.6 shows that the DNA and total protein removal capability of CIM
OH is fairly consistent at both pH 7 and pH 8. The recovery of infectious virus, while
variable, appears to be above 66% on average. The median infectious viral recovery
when comparing all the runs at both pH values is 70%, while the mean recovery is 67%.
The limited qPCR-based recovery data (only 3 data points are available, Table 6.6 ) is in
close agreement with the recovery data generated using the NTA. The qPCR protocol
was designed to quantify intact viral particles by adding a nuclease treatment step to
remove free viral DNA from the sample prior to analysis. More data is required to make
accurate conclusions; however, these results may indicate that CIM OH is able to
remove intact but non-infectious virus particles from the feed stream.
Figure 6.9 shows SEM images of the CIM OH load, flow through and elution fractions.
Viral aggregation in the presence of ammonium sulfate can clearly be seen in the CIM
OH load. Particles larger than 6 µm were present across all grids analysed. A large
number of other particles, appearing to be associated with the virus, are present in
Figure 6.9B. We speculate that this is DNA, as the particles resemble “beads on a
string” which is often used to describe heterochromatin. The “beads” are nucleosomes
which consist of DNA wrapped around eight histone proteins known as a histone
octamers. This hypothesis is supported by CIM OH mass balance data, as 60-80% of
the DNA loaded onto the columns is recovered in the flow through fraction. The
associated particles in the SEM images are shown again in Figure 6.9.C to be present at
a seemingly high concentration in the flow through fraction. They do not appear to be
present in the elution.
200
0 5 0 1 0 0 1 5 0
0
5 0
1 0 0
1 5 0
S E M L O A D
L a rg e V iru s
A g g re g a te
(N H 4 ) 2 S O 4 C ry s ta l
A ) C h r o m a to g r a p h y L o a d M a te r ia l
1 m0 5 0 1 0 0 1 5 0
0
5 0
1 0 0
1 5 0
S E M L O A D A G G
V iru s
D N A
1 m
B ) C h r o m a to g r a p h y L o a d M a te r ia l
0 5 0 1 0 0 1 5 0
0
5 0
1 0 0
1 5 0
S E M F T D N A
D N A
C ) F lo w T h r o u g h
0 .1 m0 5 0 1 0 0 1 5 0
0
5 0
1 0 0
1 5 0
S E M E L S M E R A G G
S m a lle r v iru s a g g re g a te
1 m
D ) E lu t io n
Figure 6.9 A) large virus aggregate in CIMOH load (post filtration) >6µm in diameter B)
Potential Viral-DNA complex in CIM OH Load (post filtration) C) DNA type structure
with associated protein in CIM OH flow though fraction D) smaller virus aggregate
(approx. 1µm) with less DNA association in CIM OH.
An illustration, based on the data available on CIM OH is shown in Figure 6.10. The
image is attempting to simplify a complex system, but shows how the NTA and qPCR
data may be able to agree on a low yield of total particles, while the infectivity assay
shows relatively high but variable infectious viral recovery. The NTA is identifying
particles of between 250 nm and 350 nm. The recovery appears to be less than 50%.
Large viral aggregates do not appear to be present, although particles above 1000 nm
are above the limit of quantification for the instrument. The fact that the NTA and
qPCR agree in terms of percentage recovery is interesting. This may be due to large
viral aggregates present in the load material binding to the column and then being
201
broken down and recovered in the elution fraction. These particles wouldn’t be
detectable in the load by the NTA, as they would be larger than 350 nm, but would be
detectable by qPCR as this is able to detect all encapsulated viral genomes. The
particles would then be detectable by both assays in the elution. This theory only works
if the majority of non-viral, host cell derived particles, of a similar size to vaccinia are
removed prior to elution. However, data from BCA, PicoGreen and SEM suggest this
may be the case. These assays show that that the concentration of contaminants in the
elution is low. If this were not the case the NTA would yield a higher recovery than the
qPCR as it is a non-specific step. Microscopy data suggests significantly less
aggregation in the elution relative to the load, however, as only small sections of a
sample can be analysed, many more images would be required in order to generate
statistical significance, but this is still supportive data. TCID50 is shown to yield a
variable but high recovery of infectious virus. It is not known whether a single
infectious viral particle can infect a cell if aggregated with other non-infectious
particles. It is speculated, however, that in a dynamic system this may be a high source
of variability. This question has been raised before, (Floyd & Sharp 1979;Kim & Sharp
1966) and it was concluded that aggregates are able to infect cells, however, how many
cells an aggregate is able to infect and how the specific composition of the aggregate
affects the infectivity is still unknown. What is worthy of note, is that aggregated
vaccinia particles appear to be more resistant to UV radiation then single particles that
have been sonic-treated to remove aggregates (Galasso and Sharp 1965). Based on the
data presented in this thesis, aggregation in the presence of ammonium sulfate can be
reversed by the addition of sodium phosphate without salt, as was shown in Figure 4.8.
Based on the NTA and TCID50 data, the approximate total particle to infectious unit
ratio (P:I ratio) in the CIM OH load is 100:1. In the elution this is reduced to 10:1. This
202
suggests that the CIM OH column is able to remove approximately 90% of the non-
infectious particles from the load.
203
Figure 6.10 An illustration to show proposed feed stream complexity and purification
output based on data from CIM OH. NTA is assumed to be able to count any particle of
close to 300nm (red and green rectangle (viral particles), and circles (cell components)),
qPCR will count all intact virus particles regardless of whether infectious or not, TCID50
will only be able to count infectious virus particles, it is unknown whether infectious
particles within an aggregate is able to infect a cell. TEM show an extremely small snap
shot of the system.
204
6.7 Conclusions
CIM OH monoliths have proven to be an effective capture and polish step for vaccinia
virus lister strain. The total protein concentration in the elution fractions were reduced
to below LLOQ levels of the BCA assays used in this study. More specific host cell
protein (HCP) ELISAs will be needed to confirm the results prior to clinical application,
but the outcome of this study is encouraging. CIM OH monoliths are also able to
remove an impressive amount of DNA without the use of Benzonase®. At pH 8 up to
99% of the DNA loaded was removed and infectious virus recovery in the elution was
approximately 90%. This corresponded to a purification factor of 88. This purification
factor is significantly higher than that achieved with CIM SO3. It was found that when
developing a capture step for CIM S03 monoliths, a trade-off existed between the
recovery of infectious virus and the recovery of DNA. This meant that the DNA
purification factor was limited to 4.3. To illustrate this, in order to remove 92% of the
DNA only 35% of the infectious virus loaded onto the monolith was recovered in the
elution. CIM SO3 monoliths were also not able to remove as much total protein. When
eluted with 300 mM NaCl a maximum of 92% of the total protein loaded was removed.
This corresponded to a total protein purification factor of 4.
A potential downside of using CIM OH as a capture step is the loss of virus across the
pre-column filtration step. The filtration step is necessary to prevent monolith fouling
which can result in high backpressures. The recovery across the 0.8 µm filter in
optimised HIC load buffer was found to be approximately 35%. This was increased
from 6% by decreasing the levels of ammonium sulfate in the load buffer. In
comparison the recovery across the same filter when loaded in CIM SO3 load buffer (50
mM sodium phosphate pH 7) is around 60 – 70%. This will need to be addressed in
future work. 35% recovery, while reasonable for the purpose of developing a capture
205
step, would make the whole process extremely expensive in terms of overall cost per
dose.
While there are many challenges associated with the development of viral processes,
two stand out as being the most significant. The first is the complexity and incredible
variability of the starting material in terms of infectious titre, concentration of DNA and
protein, and levels of polydispersity in relation to particle size. The second is the poor
quality of the available analytical assays. No single assay is able to tell the “whole
story” in terms of virus concentration and many have a routine intra-sample variability
greater than 30%. Both CIM SO3 and, in particular, CIM OH appear to be able to
adsorb a relatively large number of viral particles, approximately 1x109
pfu/mL of
monolith. Both chemistries have demonstrated the ability to remove the majority of
contaminants, both removing over 90% of DNA and Protein, and recovering a
reasonably large percentage of infectious virus, up to 50% from CIM SO3 and up to
90% from CIM OH.
By employing a number of orthogonal analytical methods, simplified illustrations of
each chromatographic system have been generated. These models are data driven and
comparable to previous work. In order to increase the levels of certainty on the
conclusions drawn, more replicates and an even greater variety of analytical
measurements need to be performed. Resource limitations prevented these from being
possible during this study; however, future work will need to include them.
206
Chapter 7
An Economic Evaluation of Monolith-
based processes and Comparison to
Parallel Technologies
7.1 Introduction
This chapter aims to compare monolith chromatography with ultracentrifugation for the
purification of vaccinia virus feed streams. This comparison is designed to firstly, assess
the feasibility of the industry moving away from ultracentrifugation. Secondly, it aims
to identify the critical parameters that will need to be focused on during future process
development of vaccinia virus in order to generate viable commercial processes. As
described in Chapter 2, there are advantages and disadvantages to both
ultracentrifugation and chromatography; hence in order to make decisions both of these
need to be addressed. To assist with this process, a decision tool in the form of a cost of
goods (CoG) model, built using BioSolve Process Enterprise Version 7 (Biopharm
Services, Chesham, Bucks, UK) was used. The data used in the model is based on the
chromatographic data generated during this EngD research project as well as published
literature on the purification of vaccinia virus using large-scale ultracentrifugation.
Process flow diagrams for the proposed ultracentrifugation and CIM monolith-based
processes are show in Figure 7.1 (Section 7.3.2).
Purification using small and large pore TFF filters has also been used previously to
purify influenza virus (Wickramasinghe, Kalbfuß, Zimmermann, Thom, & Reichl
207
2005), however, this has not been focused on in this chapter as question marks arise
over this unit operation’s ability to remove enough contaminates for high dose
applications (Greenberg and Kennedy 2008;Monath, Caldwell, Mundt, Fusco, Johnson,
Buller, Liu, Gardner, Downing, Blum, Kemp, Nichols, & Weltzin 2004).
7.2 Model Design and Setup
7.2.1 Cost breakdown
BioSolve Process Enterprise version 7 (Biopharm Services, Chesham, Bucks, UK) was
used as a platform to build the whole process model. Cost data was taken from
BioSolve’s own data base unless otherwise stated. Exact unit prices for cGMP
monoliths cannot be disclosed. This information is considered proprietary by BIA
Separations; however, an up-to-date price list supplied BIA Separations was used in
building this model.
The cost of goods breakdown considered in this study is shown in Table 7.1
208
Table 7.1 Breakdown of costs and their associated components
Grouped Costs Components
Capital General equipment
Process equipment
Cost of works
Materials + QC
Media
Buffers
Direct Raw Materials
Bought WFI
CIP
QC tests
Consumables
Monoliths Columns
Bags
Filters
Other
Labour
Process
Quality
Indirect
Other
Insurance/other
Waste management
Maintenance
Utilities
7.2.2 Key equations and model rules
During small scale studies in T175 flasks CV-1, cell productivity was measured at
approximately 100 pfu/cell. It is unknown whether this would scale up when producing
vaccinia virus using microcarriers in stirred tank reactors, however, as a starting point,
this value was carried through.
209
The following equation was used to estimate viral titre:
𝑇𝑖𝑡𝑟𝑒 = 𝑀𝑐 𝑥 𝑀𝑑 𝑥 𝐶𝑚 𝑥 𝑉𝑃𝑐 7-1
where Mc is microcarrier surface area in cm2/g, Md is microcarrier seeding density in
g/L, Cm is the average number of cells per microcarrier in cells/cm2
and VPc is the total
viral particles produced per cell in pfu/cell.
cGMP compliant monoliths are available in three sizes, 0.08 L, 0.8 L and 8 L. In order
to determine the size of monolith capsule required the following rule was applied.
𝐼𝐹 (𝑃𝐿
𝐷𝐵𝐶 𝑉𝑐 𝑁) => 1 … .use larger column 𝐸𝐿𝑆𝐸 𝑢𝑠𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑠𝑖𝑧𝑒. 7-2
where PL is the number of vaccinia particles in the feed stream in pfu, DBC is the
dynamic binding capacity of CIM monoliths, which is set to 1 x 1012
pfu/L, Vc is the
monolith volume in L, and N is number of cycles allowed (set manually).
This rule starts with the smallest monolith which is 0.08 L and will work up each
available size until it gets to the largest of 8 L.
Labour costs are broken down into direct hours, solution preparation, and cleaning.
Process operator and supervisors also incur indirect costs to the total.
∑ 𝑳𝒂𝒃𝒐𝒖𝒓 𝒄𝒐𝒔𝒕 7-3
= ((𝑑𝑖𝑟𝑒𝑐𝑡 ℎ𝑜𝑢𝑟𝑠 × 𝑟𝑒𝑚𝑢𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑝𝑒𝑟 𝑤𝑜𝑟𝑘𝑒𝑟)
+ 𝑢𝑛𝑖𝑡 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 𝑝𝑟𝑒𝑝 𝑐𝑜𝑠𝑡𝑠
+ 𝑢𝑛𝑖𝑡 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑐𝑙𝑒𝑎𝑛𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠) + 𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝑐𝑜𝑠𝑡𝑠
210
where indirect costs are made up of logistics, engineering and ‘other’ costs and are
assumed by BioSolve to be 38% of the total production operator and supervisors direct
costs.
Other costs, shown in Table 7.1, are made up of insurance, waste management,
maintenance and utility costs.
Insurance, utilities and maintenance are calculated as a percentage of the total facility
costs, as being 1%, 3% and 5% respectively.
Waste management is broken down into non-contaminated and contaminated, aqueous
and plastic waste. The associated disposal costs of each are shown in the appendix.
As shown in Figure 7.1 later in this chapter, both the ultracentrifugation and monolith-
based processes included two TFF steps.
The surface area of the filter is determined using the following equation.
𝐴 =𝑉𝑝𝑇
𝐽𝑝 7-4
where 𝑉𝑝 is permeate volume in L, 𝐽𝑝 is flux in L m-1
h-1
and 𝑇 is process time in h.
Total capital costs are made up of the components shown in Table 7.1. This number is
assumed to be borrowed upfront. The capital cost per batch and per dose are then
calculated from the estimate of capital costs per year. Annual capital is the amount
needing to be paid back to the loan provider and is calculated using the following
equation.
𝐴𝑛𝑛𝑢𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 = 𝑃𝑅
1−(1+𝑅)−𝑁 7-5
211
where P is the total capital cost, R is the fixed periodic interest rate, assumed to be 12%
per year and N is the number of years required to pay back the loan. This is assumed to
be 10 years.
7.3 Model Implementation
7.3.1 Key Assumptions
The base case assumptions made in the model are shown in Table 7.2.
Table 7.2 Ultracentrifugation and CIM Monolithic base case assumptions
The capacity achievable on a KII ultracentrifuge using a K3 rota is reported by the
manufacturer as being up to 200 L (Alfa Wassermann 2013). Previous work reported in
the literature has shown that approximately 20 L of vaccinia broth can be processed
with an RK-centrifuge in an RK3 rota with percentage recovery of approximately 60%
KII Ultracentrifugation Monolith
Capacity 20 L/cycle 1x1012
pfu/L
Flow rate 0.2 L/min 1 CV/min
Hold time 1st purification unit operation 12 hr 0 hr
Hold time 2nd
Purification unit operation 1 hr 0 hr
Number of reuses Infinite (cannot be cycled) 20 cycles
Percentage Virus Recovery
Bioreactor Volume
60%
100 L
60%
100 L
212
(Hilfenhaus et al. 1976b). The RK system is now obsolete, but the equivalent rota is
available from Alfa Wassermann and has since been scaled up. It is likely that volumes
greater than 20 L can be processed in the new rota as its volume has been increased
from 1.6 L to 3.2 L. It is also assumed, based on data in the same publication, that two
ultracentrifugation steps will be required. Virus material is pumped into the rota and
then left for a set period of time for virus to band in the sucrose bed. In the base case
scenario, the first banding occurs in 12 hours while the second takes 1 hour. Smaller
viruses can take several days to band; therefore a maximum of 50 hours for each
(ultracentrifugation) unit operation has been used in the comparative analysis section of
this chapter (Figure 7.5).
Based on work with CIM SO3 and CIM OH monoliths, a dynamic binding capacity of
1x1012
pfu/L is a reasonable assumption for large-scale vaccinia processes. This
assumes that the 1 mL monoliths scale linearly. As large-scale monoliths can be run up
to a maximum velocity of 1 CV/min, this flow rate has been fixed for all sensitivity and
scenario analyses. The percentage recovery of infectious virus was shown to be above
60% from both CIM SO3 and CIM OH, therefore the base case infectious recovery was
fixed at 60%. The production volume was set to 100 L. In reality this would depend on
the therapeutic application, the efficacy of the therapy and the market demand. As these
are unknown it was decided to indiscriminately set a limit and keep this constant during
all comparative studies. A table of main costs is shown in Table 7.3. Key personnel
remuneration costs are show in Table 7.4.
213
Table 7.3 Lists of key process costs. The “General process” costs category comprises costs
specific (referring) to unit operations that are the same regardless of the purification
strategy. This includes upstream, primary recovery and final formulation costs. *
Monolith costs are confidential so have been approximated.
Process Step Unit cost
General process 100 L bioreactor 200,000 USD
Depth filters 300 USD/m2
UFDF filters 3000 USD/m2
Bags
• 20L
• 50L
• 100L
• 200L
420 USD
450 USD
520 USD
660 USD
Media serum free
(DMEM/F12)
Buffers (DPBS)
In vitro virus assay (infectivity)
3.31 USD/L
0.73 USD/L
13,000
USD/assay
Monolith chromatography
process
Chromatography rig 220,000 USD
Monoliths*
• 0.08 L
• 0.8 L
• 8 L
~5,000 USD
~10,000 USD
~30,000 USD
Ultracentrifugation
process
Ultracentrifuge
350,000 USD
214
Table 7.4 Personnel remuneration costs per year (BioSolve Process Enterprise)
Personnel Remuneration (USD/Year)
QC 74,400
QA 97,650
Supervisors 100,750
Operators 62,000
A list of more general process assumptions made in relation to both monolith and
ultracentrifugation processes are listed below.
• Each process is able to generate material to the required regulatory standards in
terms of purity.
• Ultracentrifuges are not cycled. Ultracentrifugation cycling occurs when a single
system is run multiple times in order to process the whole batch. If an
ultracentrifuge reaches capacity and additional material requires purification, the
batch will be split and run in multiple machines in parallel. The reason behind this
assumption is given in Section 7.3.2 Process Flow and Facility Design.
• Infectivity analysis (in vitro) is required after every unit operation. If a batch is split
in two, each new feed stream is analysed independently before being pooled back
together.
• A new facility will be built with new equipment. Capex will be spread across a 10
year duration in which it will lose 90% of its value.
• Treatment requirements are five injections of 1x109 pfu/injection. This makes the
total treatment requirements of 5x109 pfu per patient. A dose is assumed to be the
total 5x109 particles.
215
• The bioreactor volume is fixed to 100 L in all scenarios unless otherwise stated. It
was assumed that the treatment was for Head and Neck cancer which had an
incidence of 58,293 patients in the UK between 2010-2013 (Public Health England
2015). The base case process would produce enough material to treat 90% (17,580)
of these patients each year.
• Viral titer is assumed to be 1.6x1011
pfu/L based on the following assumptions
(Simaria et al. 2014):
Microcarrier surface area (Mc) = 2,530 cm2/g
Microcarrier seeding density (Md) = 6.3 g/L
Total cells per microcarrier surface area (Cm) = 1x105 cells/cm
2
Total viral particles produced per cell (VPc) = 100 pfu/cell
• The cost of microcarriers has not been taken into account in the model but is
assumed to be constant as bioreactor volume is fixed.
• The model does not take into account the bioreactor seed train, but starts from 100L.
• All prices have been converted to USD using exchange rates described in the
Appendix.
• All media and buffers are made up on a per batch basis.
• Single use systems are used for the following:
Bioreactor
All filtration systems
Media, buffer and product hold tanks
• A campaign is designed to last 12 months with 21 days of maintenance and 14 days
set aside for validation.
• Each shift is 9 hours long.
216
• The base case batch duration for the monolith-based process is 11 days. The
ultracentrifugation process takes 10 days.
• The number of batches produced per year for each base case is estimated to be 30.
• Batch failure rate is set to 5%.
• The final formulation buffer is assumed to be Tris-HCL plus sugars such as sucrose
(Ungerechts et al. 2016).
The default model parameters set out in BioSolve in terms of capital breakdown, waste
management, solution preparation time, cleaning, facility and maintenance costs are
kept as default and are all shown in the Appendix.
7.3.2 Process Flow and Facility Design
The model compares two proposed processes. The first has two chromatographic steps,
both of which are CIM monoliths; the other has two ultracentrifugation steps. The two
ultracentrifugation steps, as discussed in Section 7.3.1, are assumed to be carried out
using a KII Alfa Wasserman ultracentrifuges fitted with a K3 rota. Proposed process
flow diagrams are shown in Figure 7.1. Each process is the same, except for the two
purification steps. The whole process sequence has not yet been tested, and so it is
partially conceptual, nevertheless has been assumed to work.
The model has been designed without specific costs associated with a full seed train in
order to reduce complexity. As the main questions asked in the analysis are focused on
determining the operational feasibility of using a chromatography-based purification
process as opposed to ultracentrifugation, it was decided that accurate determination of
exact costs per dose were not the primary concern. What was important was to
accurately determine comparative trends and trade-offs associated with critical process
217
parameters known to be variable in both ultracentrifugation and chromatographic
processes.
The primary recovery steps in the processes described are based on unit operations
being designed at UCL (Personal Communication 2016). The process uses a tangential
flow filtration step to concentrate and lyse CV-1 cells (grown on microcarriers) in the
presence of TritonX-100, a non-ionic surfactant commonly used to chemically lyse cells
(Giard et al. 1977;Wang and Ouyang 1999). The model assumes that an average
transmembrane flux of 50 LMH is achievable, the harvest is concentrated 20 times and
then diafiltered 7 times in buffer containing TritonX-100. After the diafiltration step, the
retentate is passed through a depth filter to remove the microcarriers and cell debris.
Depth filtration is used in many processes to remove whole cells, and has been used for
clinical grade vaccinia virus to remove cell debris in the past (Ungerechts, Bossow,
Leuchs, Holm, Rommelaere, Coffey, Coffin, Bell, & Nettelbeck 2016). The primary
recovery steps have been kept the same for each process so that any trends seen are
known to be resulting from changes made to the purification steps.
It is assumed that the first TFF step will buffer exchange the feed stream into the
appropriate load buffer for the first chromatography step. The elution from the first
chromatography step is then diluted 1:5 before the second column. Monoliths run at
high flow rates (1 CV/min) so this increase in volume is not a limiting factor in the
process, as it will only marginally increase the overall processing time of the second
step. The reason why an additional UFDF step was not preferred over an inline dilution
is that it was considered unnecessary. An additional UFDF step would likely reduce the
overall process recovery and increase the processing time. This would decrease
productivity and increase costs. The final TFF step is designed to concentrate and then
buffer exchange the virus into its final formulation buffer.
218
Figure 7.1 A) Proposed ultracentrifugation process flow diagram B) Proposed CIM
monolith process flow diagram
A
B
219
One of the main process assumptions is that ultracentrifuges cannot be cycled in the
same way as chromatography columns. The reason for this is that system setup and shut
down is a very manual process and takes most of a 9-hour shift. It is assumed that an
operator would need to leave the system running overnight for approximately 12 hours,
although some viruses can take longer than 24 hours to band. Shutting the system down
and fractionating the bed, not to mention cleaning and sterilising the system ready for
reuse, is assumed to take at least a day. This would imply a considerably long holding
time for this labile viral product at 2-8 ⁰C. Vaccinia would likely start to lose infectivity.
Instead, a batch is split and run in multiple machines in parallel. The downsides of this
option are an increase in process equipment costs and an overall higher investment due
to the requirement of larger facilities. The option of running ultracentrifuges in parallel
needs to be taken into account in the design stage of the facility because a process
change of this calibre cannot be easily retrofitted into an existing facility unless enough
extra space existed into the DSP suite. Two example fit outs are shown for the
ultracentrifugation process in Figure 7.2. As the majority of closed process DSP
products are handled in grade C clean rooms, this would be very expensive and hence is
unlikely. BioSolve Process Enterprise does not take the added design change into
account when adding new equipment but calculates the total clean room space
requirements based on equipment size.
220
Figure 7.2 A) Facility fit out with a single ultracentrifuge B) Facility fit out with four
ultracentrifuges running in parallel.
Raw Materials In
Product Out
US/DS Stores
Buffer/Media Prep
DSP
Formulation
WasteCorridor
TechCorridor
3.00
Reagent Prep
QC
WasteColection
USP
Raw Materials In
Product Out
US/DS Stores
Buffer/Media Prep
DSP
Formulation
WasteCorridor
TechCorridor
3.00
2.6
2
Reagent Prep
QC
WasteColection
USP
3.00
A B
Ultracentrifuges
221
7.4 Comparison between Chromatography using CIM Monoliths and
Ultracentrifugation
7.4.1 CIM Monolith and Ultracentrifugation Base Case Process Costs
Breakdown
The ultracentrifugation and chromatography base case processes described above have
been modelled and compared against each other. Figure 7.3 shows a cost breakdown for
the ultracentrifugation and monolith processes in terms of total annual cost of goods. It
is immediately obvious that the overall costs are similar. The ultracentrifugation process
is estimated to cost $8.3 million per year, while the monolith process is estimated to
cost $8.2 million per year.
222
Figure 7.3 Costs breakdown of base case chromatographic and ultracentrifugation-based
processes
Capital is seen to be a major cost in both processes, a breakdown of which can be seen
in Figure 7.3. The reason for this is that both base case processes require a similar
amount of equipment so costs associated with build, purchase, fit out, installation and
validation do not vary much between processes, but are in each case significant. The
ultracentrifugation process requires an additional centrifuge whilst the monolith process
design assumes the requirement of two chromatography rigs, therefore the capital cost
of the former process is only slightly higher than that of the monoliths process.
Materials and QC (which BioSolve groups together) is shown to be the other major cost.
The majority of this cost comes from the QC. In vitro virus testing is required for each
U ltra c e n tr ifu g a tio n
3 3 .9 1 % C a p ita l
4 2 .3 7 % M a te r ia ls + Q C
1 0 .0 9 % C o n s u m a b le s
4 .2 7 % L a b o u r
9 .3 5 % O th e r
T o ta l = 8 ,2 5 4 ,3 1 4 U S D
M o n o lith
3 1 .1 4 % C a p ita l
3 7 .5 4 % M a te r ia ls + Q C
1 7 .5 0 % C o n s u m a b le s
5 .2 0 % L a b o u r
8 .6 2 % O th e r
T o ta l = 8 ,1 7 5 ,6 1 0 U S D
Cap
ita l
Ma te
r ia ls
+ Q
C
Consu
mab
les
La b
our
Oth
e r
0
1 ,0 0 0 ,0 0 0
2 ,0 0 0 ,0 0 0
3 ,0 0 0 ,0 0 0
4 ,0 0 0 ,0 0 0
B re a k d o w n
An
nu
al
Co
st
($)
U l t r a c e n tr i fu g a t io n
M o n o l i th
223
in process product fraction, and is estimated by BioSolve to cost 13,000 USD per assay.
Buffer and media costs are not particularly large as the bioreactor volume and total
buffer volume requirements are relatively small.
As the ultracentrifugation process splits the batch if the percentage utilisation of a single
rota reaches over 100%, extra infectivity assays (one for each product fraction) are
assumed to be required. For this reason, the materials and QC costs are slightly higher
for the ultracentrifugation process than for the monolith process. Labour costs are
shown to be slightly higher when using monoliths as 13 cycles are required in order to
process all the material. A monolith process also results in a higher consumable cost as
the resins are expensive. The most expensive consumable costs associated with the
ultracentrifugation process are the TFF filters, the total surface area requirement of
which being the same for both base case processes.
As shown in Table 7.1, the costs labelled ‘other’ include insurance, waste management,
maintenance and utilities. The reason why these are slightly higher for
ultracentrifugation than for chromatography is the slightly higher capital cost.
224
7.4.2 CIM Monolith and Ultracentrifugation Process Sensitivity Analysis
In order to test the assumptions made in the base case model, Section 7.3.1, a sensitivity
analysis was run for a number of general and purification technology specific factors
thought likely to influence the cost of goods. The rational here was to gain
understanding of the significance of the effects the fluctuation of these factors have on
CoGs. The factors tested are presented in Table 7.5. The results are shown in Figure 7.4.
225
Table 7.5 Factors changed in the Ultracentrifugation and Monolith sensitivity analysis. Best case, base case and worst case scenario values are
shown
Factors
Changed
Range and Base Case Values
Ultracentrifugation Process Monolith process
Low Base High Low Base High
Capacity 1 L 20 L 200 L 5x1011
pfu/L 1x1012
pfu/L 3x1012
pfu/L
Flow Rate 1 L/h 12 L/h 60 L/h 1 CV/min
Number of
Reuses
cannot be recycled
1 20 40
Percentage
Virus
Recovery
20% 60% 100%
20% 60% 100%
Bioreactor
Volume
10 L 100 L 200 L 10 L 100 L 200 L
226
Figure 7.4 Tornado diagrams showing the effects of changes in the base case variables
shown in Table 7.5 on the overall costs per dose. A) Shows factors affecting CoGs for
ultracentrifugation processes. B) Show factors affecting CoGs for monolith processes. All
factors were changed independently with all other conditions remaining the same as in the
base case. Blue bars represent a reduction in costs per dose while red bars represent an
increase.
-1,0
00 0
1,0
00
2,0
00
3,0
00
4,0
00
5,0
00
V iru s T itr e
B io re a c to r V o lu m e
P e rc e n ta g e V ira l R e c o v e ry
N u m b e r o f C o lu m n u s e s
D B C (1 c o lu m n u s e )
D B C (2 0 c o lu m n u s e s )
C h a n g e in c o s t p e r d o s e ($ )
A
B
-1,0
00 0
1,0
00
2,0
00
3,0
00
4,0
00
5,0
00
V iru s T itr e
B io re a c to r V o lu m e
P e rc e n ta g e V ira l R e c o v e ry
V o lu m e tr ic T h ro u g h p u t
F lo w R a te
C h a n g e in c o s t p e r d o s e ($ )
H igh L ow
H igh L ow
227
There were a number of reasons for choosing the factors shown in Table 7.5. Firstly, it
was thought that they would have a significant effect on the CoGs. Secondly, they are
all parameters that can theoretically be optimised during process development. Should
trade-offs that include any of the factors analysed arise during process development, an
understanding of their effects on the CoG relative to each other can also help when
making process development decisions. A hypothetical example of this might be a
trade-off between loading capacity and the number of possible reuses of a monolith
column. The results of a sensitivity analysis can be used to set priorities in terms of
which parameter to favour.
The capacity achievable when developing ultracentrifugation or monoliths-based
processes is likely to be one of the first major questions asked in process development
(Hilfenhaus et al. 1976a;Hilfenhaus, Kohler, & Gruschkau 1976b). The ranges shown in
Table 7.5 are based on both literature and vaccinia process development data. In terms
of flow rate, large-scale monoliths can be run at a maximum flow rate of 1 CV/min. As
mass transfer is not limited by diffusion, comparison studies looking at the effects of
different flow rates on binding and resolution are unlikely to be on the critical path,
therefore the decision was made not to include any variation on flow rate into the
sensitivity analysis for monoliths. The range and base case values chosen for the
ultracentrifugation process come from values specified by the manufacturer, and would
need to be confirmed experimentally. The number of reuses possible when using
monoliths are, again, based on the manufacturers best case assumptions. As described in
Section 7.3.1, the model assumes that ultracentrifuges cannot be cycled. For this reason,
ultracentrifugation cycling is also not included in the model. Percentage recovery of
virus particles was assumed to have a large impact on CoGs for both processes. While
results vary from virus to virus and from process to process, it is difficult to conclude
that either one of the processes being compared would likely be superior to the other in
228
general terms when it came to percentage recovery. Data in the literature, as well as data
generated for this thesis, shows that virus yield is extremely variable on both systems.
Between 20-100% recovery of infectious virus is recorded in the literature (Gerster,
Kopecky, Hammerschmidt, Klausberger, Krammer, Grabherr, Mersich, Urbas,
Kramberger, Paril, Schreiner, Nobauer, Razzazi-Fazeli, & Jungbauer 2013b), therefore
this range was used with a base case recovery in each case set at 60%. Bioreactor
volume and virus titre were included in the analysis for two reasons. Firstly, for
completeness but secondly, to act as a reference point in terms of relative impact. It is
well known that titre has a massive impact on the overall CoGs per dose when varied,
so the effects of other factors can be compared to this change to enable a thorough
evaluation of potentiation impact.
7.4.2.1 Factors Identified in the Sensitivity Analysis to Affect Cost of
Goods
As expected, changes in viral titre and bioreactor volume have the most significant
effects on the CoGs as seen in Figure 7.4. The reason for this is that the number of viral
particles produced per unit volume, as well as the total volume of starting material
available per batch if the titre remains constant, directly affects the number of doses
produced annually. This outweighs any increases in cost associated with handling the
extra material.
The cost of an ultracentrifugation process is significantly affected by the volumetric
capacity of the machine. Batch ultracentrifuges have significant load volume limitations
as each rota has a fixed volume and this cannot be surpassed. Continuous flow
ultracentrifuges are designed to be continuously loaded at a pre-defined flow rate until a
maximum capacity is reached. The machine is then typically left for periods of time
229
ranging from several hours to several days to allow virus to band. The overall
volumetric throughput is dependent on the total mass of impurities with a similar size
and buoyant density to the virus. When capacity is reached, the purification factor starts
to drop off quickly as banded virus and impurities start to overlap. The reason why the
volumetric throughput has such a significant impact on the CoGs is directly related to
one of the primary assumptions being made about ultracentrifugation in the base case
model. This is that an ultracentrifuge cannot be cycled when run at large-scale. One of
the main reasons for this is that viral stability would become a limiting factor as the
process would take a long time to run. If this assumption is acceptable, and a centrifuge
is unable to process the whole volume in a single cycle, then a batch would need to be
split and run in multiple machines in parallel. Consequently, in the case of low
achievable volumetric throughput, this would result in a significant increase in the
capital cost of the process.
Depending on the stability of the virus being processed and the total cycle time of each
ultracentrifugation step, the supposition that ultracentrifuges are not able to be cycled
may become debatable. In order to evaluate the potential costs per dose of running an
ultracentrifugation step in multiple cycles a scenario analysis was run assuming
different processing times with and without cycling. The output was plotted as a
function of volumetric throughput. This data is presented in Figure 7.5.
230
Figure 7.5 Effects of Ultracentrifugation cycling and process time on the CoGs per dose.
Time periods represent the first and second ultracentrifuge step process times. Cycling
Possible = batches are cycled on one machine. No Cycling Possible = batches are split and
run on multiple machines if required. Base case is assuming a 20 L vol. throughput for
12hr in the first system and 1hr in the second and cannot be cycled (Blue line in bold type
script).
In reference to the base case ultracentrifugation process (as shown in Figure 7.3) cycle
times used in the model were taken as reported in the literature (Hilfenhaus, Kohler, &
Gruschkau 1976b). In this paper virus was loaded into the KII ultracentrifuge at 12 L/h
and left to band overnight. The next day virus was fractionated and processed once
again but this time left to band for an hour only. 50 hours as an assumption is an
exaggeration, but shows the effects of cycling on the cost of goods as the banding time
increases. Other sources suggest that viruses such as Rabies will band in as little as 8
hours (Kumar et al. 2002).
The costs are shown to increase exponentially (Figure 7.5) when the volumetric
throughput of an ultracentrifuge is low. This is seen to be the case even if the machines
can be cycled, although the negative impact on cost is lower. This is illustrated when
0 5 0 1 0 0 1 5 0 2 0 0
0
1 ,0 0 0
2 ,0 0 0
3 ,0 0 0
U ltra c e n tr ifu g a tio n V o lu m e tr ic T h ro u g h p u t (L /b a tc h )
Co
G/d
os
e (
$) 1 2 h r 1 h r C y c l in g P o s s ib le
1 2 h r 1 h r N o C y c l in g P o s s ib le ( B a s e C a s e )
5 0 h r 5 0 h r C y c l in g P o s s ib le
2 4 h r 8 h r C y c l in g P o s s ib le
231
comparing ultracentrifugation banding times of 12 hours followed by 1 hour with and
without cycling (green and blue lines Figure 7.5). If an ultracentrifuge can be cycled,
then the additional capital of purchasing multiple machines would not need to be spent,
however, the downstream process would take significantly longer, and this would
become a bottleneck (Morenweiser 2005). This point is emphasised when the
volumetric throughout is below 20 L/batch.
It can be seen in all cases that when volumetric throughput increases above ~25 L per
batch (Figure 7.5) the costs become very similar. The reason for this is that a single
ultracentrifuge is able to process all the material, therefore it makes little difference
whether it can be cycled or not. As only a single bioreactor is used in the model,
BioSolve schedules the campaign by taking the total number of weeks the facility is in
operation and dividing that number by the total time taken to complete the longest stage
of the process. This is then multiplied by the percentage facility utilisation. In most
cases the bottleneck in terms of time is in the upstream process (197 h). The campaign
is scheduled by running the time bottle neck unit operations in series. Even if the
ultracentrifugation process takes 50 hours, this unit operation would not become the
bottleneck unless the volumetric throughput was lower than 20 L. At high volumetric
throughput, the only significant impact compared to base case assumptions would be
labour. However, labour as shown in Figure 7.3, does not make up a significant
proportion of the CoGs. Capital and raw material costs remain the same as a single
ultracentrifuge is run once to purify all the material. The impact of cycling the
ultracentrifugation step assuming a 50-h run time on whole process timelines is shown
in Figure 7.6. All other base case assumptions are kept constant.
232
Figure 7.6 Production Gantt chart to show time requirement for each unit operation in
days. This assumes each ultracentrifuge requires 50 h to band vaccinia.
Variations in the infectious virus recovery have, as expected, a significant effect on the
cost of goods per dose for both chromatographic and ultracentrifugation-based
processes. In a similar way to the effects seen on costs when viral titre and reactor
volume are increased, more doses are produced per batch when recovery increases.
Interestingly, as shown in Figure 7.7, when costs are analysed as cost of goods per batch
rather than per dose for the monolith process, the trends seen are reversed. This is
because the lower recovery reduces the number of particles needing to be processed,
which results in less media being required, or fewer cycles of a given amount of resin
per batch being needed. This reduces both the consumable and labour costs. This is not
the case for ultracentrifugation as throughput is based on volume, and therefore
assumed not to be affected by the number of infectious viral particles in the load.
0 5 1 0 1 5
T F F
U ltra c e n tr ifu g a tio n
D ilu t io n ( if re q u ire d )
U ltra c e n tr ifu g a tio n
D e p th F il t ra t io n
T F F
P ro d u c tio n
T im e (D a y s )
Un
it O
pe
rati
on
s
233
The reason why the increase in recovery still leads to a drastic decrease in the CoG/dose
is that the increase in CoG/batch is fairly marginal. An increase in percentage recovery
from 20-100% elevates the CoG/batch by approximately 10%.
0 5 0 1 0 0
0
5 0 0
1 0 0 0
1 5 0 0
2 5 0 ,0 0 0
2 6 0 ,0 0 0
2 7 0 ,0 0 0
2 8 0 ,0 0 0
2 9 0 ,0 0 0
3 0 0 ,0 0 0
% V iru s R e c o v e ry
Co
G/d
os
e
Co
G/b
atc
h
C o G /d o s e
C o G /b a tc h
Figure 7.7 Effects of percentage virus recovery from the first of the two chromatography
steps analysed as total cost per dose and costs per batch
Labile viruses have a tendency to lose infectivity during chromatographic processing,
especially at extremes of salt concentration. The recovery of infectious virus from
chromatographic systems is therefore an important performance attribute requiring
attention during process development.
One of the disadvantages of using chromatography is that resins are expensive,
especially if a large volume is required in order to process the material required. Factors
that influence this cost other than the amount of material required, which is often fixed,
is how much material can be loaded onto the resin before its capacity is reached, and
how many times the resin can be re-used. As shown in Figure 7.8, the effects of each
234
factor on the cost of goods are related. There is an inversely proportional relationship
between the number of reuses and the cost of goods. This is shown under base case
assumptions in Figure 7.8A. There are three cGMP compliant commercially available
CIM monoliths, these include 0.08 L, 0.8 L, and 8 L units. The cost of goods model was
designed to pick the smallest column that will process the whole batch. Figure 7.8B
shows the effects of increasing DBC on the cost of goods per dose. The graph shows 3
different values as the DBC is increased. An 8 L column is required for the lowest DBC
tested; this then changes to an 0.8 L and eventually to an 0.08 L monolith.
What can also be seen when analysing Figure 7.8B is that these values are also
dependant on the number of times the monoliths can be recycled. The effect of a
column’s inability to be recycled on the CoGs is much greater when the DBC is low, as
significantly more resin is required. The data also suggests that when the columns can
be recycled more than 10 times the DBC has little effect on the CoGs. This is because
all the material can be purified using a single small 0.08 L monolith column. Moreover,
the base case process requires 13 cycles on a 0.08 L monolith, however, as monoliths
are run at high flow rates this has minimal impact on the overall batch processing time
and so does not impact the total number of batches produced during a 12-month
campaign. Based on this data it can be concluded that the number of reuses is more
important than the relatively small changes in the DBC in reducing overall CoGs/dose.
The cheapest processing option is to cycle smaller columns 10 or 20 times rather than
run a single large column to capacity.
235
Figure 7.8 Effects of DBC and reusability of CIM Monoliths on CoG per dose.
The experimental data in this thesis suggests that CIM SO3 monoliths may yield less
infectious virus in the elution fraction after multiple uses. This might be the result of
monolith fouling, and may be linked to the amount of total DNA in the load as
described in Section 5.5.2. CIM OH monoliths by comparison do not appear to be
0 1 .01 0 9 2 .01 0 9 3 .01 0 9 4 .01 0 9
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
D B C
Co
Gs
/do
se
($
)
1 0 u s e s
1 u s e
2 0 u s e s
0 1 0 2 0 3 0 4 0 5 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
N u m b e r o f u s e s
Co
Gs
/do
se
($
)
A
B
236
affected by the number of previous uses and yield a purer viral prep than CIM SO3, as
described in Section 6.7. Both monoliths have similar DBCs for vaccinia virus, although
CIM SO3 shows more variability in this respect with potential DBC values ranging
from 1x109
to 3x109 pfu/mL, (Figure 4.6). In the event that either chemistry is assessed
as an initial capture step for vaccinia virus, this cost data can be combined with the
experimental data obtained in order to identify drivers for future process development.
An example of this might be the decision to work on increasing the binding capacity
and yield of CIM SO3 but accepting that this type of monolith might only give robust
and reliable results once and will therefore need to be used as a single use resin.
7.5 An Economic Comparison between Chromatography using CIM
Monoliths and Ultracentrifugation.
The data in Table 7.6 summarises the advantages and disadvantages of
ultracentrifugation and chromatography in comparison to each other. Both systems are
able to remove impurities so purity is not generally a bottleneck. The reason percentage
yield has been placed as an advantage for ultracentrifugation is that particles do not
have anywhere to go. It has been reported that infectivity can be lost in some instances
depending on the stationary phase used (Gias, Nielsen, Morgan, & Toms 2008), but
recoveries equivalent to optimised chromatographic systems have been reported for
many years (Hilfenhaus, Kohler, & Gruschkau 1976b).
237
Table 7.6 Advantages and disadvantages of ultracentrifugation and chromatography and
filtration as purification unit operations for viral vaccines. Yield has been coloured red as
the recovery of infectious virus from each unit operation is dependent on the type of virus.
Ultracentrifugation Chromatography
Advantages Purity
Yield
Purity
Scalability (DBC)
Disadvantages Cost
Scalability
(Throughput)
Cost
Yield (requires optimisation,
trade-offs exist)
One of the major differences between chromatography and ultracentrifugation is the
trade-off between ultracentrifugation throughput and chromatography percentage yield
versus product purity. During chromatographic development conditions that maximise
product recovery tend to also maximise the recovery of impurities which limits the
purification factor achievable. Ultracentrifugation percentage recovery is dependent on
banding efficiency. The separation of product from contaminating impurities is often
high if there is a significant difference in buoyant density between the different
components. When the difference is small, the bands can overlap which reduces the
purification factor achievable. In practical terms, this will impact the amount of material
that can be loaded onto the bed as increasing the loading capacity reduces the separation
efficiency. The data in Figure 7.9 shows a direct comparison between the trends in
CoGs/dose from the ultracentrifugation and monolith-based processes when the
volumetric throughput of the ultracentrifuge, and the recovery of infectious virus from a
monolith is varied. The graph shows that the costs overlap assuming that 60% recovery
of infectious virus can be achieved without optimisation from a large-scale
ultracentrifuge. This would need to be amended if not the case. When the percentage
238
recovery is below 50% from the monolith, the ultracentrifugation process is cheaper
unless the volumetric throughput decreases below ~5 L. The cost increases
exponentially when additional machines need to be purchased. This makes even lower
yielding chromatography processes more cost effective than ultracentrifugation
processes. A monolith process is shown to become less expensive than
ultracentrifugation when percentage recovery increases above 50% regardless of the
volumetric throughput achievable using an ultracentrifuge. It should of course be noted
that the percentage recovery achievable from the ultracentrifuge will impact where the
two graphs intersect.
Figure 7.9 Overlay in overall CoG per dose to show how trade-offs in critical process
parameters can shift the balance from chromatography to ultracentrifugation. Monolith
percentage recovery is varied as trade-offs exist between virus recovery and purity. In
continuous flow ultracentrifugation the trade-offs are likely to be between purity and
volumetric throughput.
0 5 0 1 0 0
V iru s r e c o v e ry (M o n o l i th ) (% )
0 5 0 1 0 0 1 5 0 2 0 0
0
5 0 0
1 ,0 0 0
1 ,5 0 0
2 ,0 0 0
U ltra c e n t r i fu g a tio n V o lu m e tr ic T h ro u g h p u t (L )
Co
Gs
/do
se
($
) C o s t p e r d o s e (M o n o l i th P r o c e s s )
C o s t p e r d o s e (U l t r a c e n t r i f u g a t io n P r o c e s s )
239
7.6 Conclusions
Both ultracentrifugation and chromatography can become prohibitively expensive.
Depending on the critical process performance attributes, which are likely to be highly
specific to the virus in question, both could be the cheaper option. As a result, it is not
possible to conclude that one technology should be used preferentially over another in
general terms. Ultracentrifugation is a tried and tested technique, and with the advances
in continues flow systems, it has become easier in theory to scale up. Chromatography
is currently the work horse in the bio pharma sector and is increasingly becoming the
method of choice to purify viruses and other large macromolecules. A major issue when
using chromatography, however, is the large number of experiments required in order to
generate optimal separation performance. This is also true when purifying proteins,
except that there are significant analytical challenges associated with quantifying viral
particles. This makes process development extremely time consuming and expensive
with robust processes difficult to generate. Ultracentrifugation does not have this issue
as there are fewer independent variables that affect the separation and that can be used
to optimise the process.
In many ways, a decision as to what technology to use will depend on the equipment
already available, especially if manufacturing in house is preferred, as well as the time
available for process development prior to cGMP material needing to be available.
In an ideal world, optimised processes for chromatography and ultracentrifugation
would be developed in parallel and then compared before a decision is taken, however
in practice this is unlikely to happen due to bottlenecks in both time and resources.
Decisions will therefore need to be made with incomplete data based on the specific
project requirements at the time and estimated for the life of the facility in question.
240
Chapter 8
Challenges associated with the
development and manufacture of
infectious vaccinia virus
8.1 Introduction1
There are a number of challenges that need to be addressed if vaccinia virus is to be
licenced and validated as an oncolytic viral therapy. According to ICH guideline Q11,
process validation involves “the collection and evaluation of data, from the process
design stage throughout production, that establish scientific evidence that a process is
capable of consistently delivering a quality drug substance” (EMA 2011). For this to be
possible it is important to identify the critical quality attributes (CQAs) early on in
process development. The ICH guidelines acknowledge that when dealing with complex
biological material this is a challenge. They state that risk management strategies should
be implemented to account for the lack of knowledge and characterisation related to the
safety and efficacy of each CQA during the lifecycle of the product.
Two of the specific challenges when dealing with infectious viral products are analytical
sensitivity and variability (Darling, Boose, & Spaltro 1998;Sullivan et al. 2012). These
characteristics make quantifying the CQAs challenging. The relative standard deviations
across each unit operation are cumulative leaving the potential variation across a
process to be as high as 60% based on the research presented in this thesis. The CQAs
and associated analytical challenges for vaccinia virus are discussed below.
1 This chapter is included as part of the UCL requirements for the award of Doctor of Engineering
241
Vaccinia virus concentration
Physical techniques such as NTA, TRPS, TEM and flow cytometry will
quantify the total number of particles of a given size, shape or physical
characteristic but are nonspecific and therefore are unable to give
accurate concentration data unless the sample is pure.
Specific assays include ELISAs, qPCR-based techniques, and Western
Blots. Some ELISAs claim to be able to identify whole viral particles
from associated viral proteins, but this is difficult to validate. qPCR-
based techniques require extensive sample pre-treatment to remove non-
encapsulated viral DNA which reduces the accuracy and precision of the
measurement. Both ELISAs and qPCR require reliable reference
standards, which are often difficult to source, and suffer from high
variability and relatively low sensitivity. ELISA reference standards are
validated using protein sequence data and TEM.
Vaccinia virus potency
The potency of a virus relates to its biological activity. For vaccinia virus
licenced as an oncolytic therapy, the simplest measure of potency is
infectivity. The reason for this is that oncolytic viruses such as vaccinia
need to be replication competent. In this thesis infectivity was measured
by TCID50, however, there are a number of other cell-based approaches
as outlined in Chapter 2. All suffer from relatively low sensitivity and
high variability.
Infectivity determines the ability of a virus to infect a cell but not
necessarily to genetically modify it. If a therapy is required to transduce
a cell and insert a gene of interest into the nucleus then transduction
assays will need to be performed.
242
Transduction assays tend to be more specific than infectivity assays
although variability associated with both techniques prevents robust
conclusions from being generated for either in isolation.
Product related impurities such as:
o Levels of vaccinia virus aggregation
Presence of aggregates can be tested using physical techniques
previously discussed, however, there is currently no way of
measuring the origin, infectivity and quantity of viral aggregates.
o Levels of vaccinia virus degradation
Degraded viral particles can sometimes be visualised using TEM
although quantification using this approach is challenging. It also
requires specialist equipment which is likely to be too expensive
to bring in-house. It may be possible to use a combination of
ELISAs to quantify non-encapsulated viral coat proteins, but this
capability would need to be developed and would be very
difficult to validate in terms of accuracy.
Process related impurities such as
o Concentration of contaminating DNA
Florescence-based assays such as Quant-iT PicoGreen can be
used for total DNA quantification but they are non-specific. The
LLOQ of PicoGreen is 250 pg/mL, which may become limiting
for some applications.
qPCR can also be used, but this will quantify specific genes
known to be conserved in the genome of the host cell. This will
therefore require assumptions to be made regarding the total
estimated mass of DNA in a sample based on the mass of DNA
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used as a standard. High variability in impure fractions is also
common when using qPCR.
o Concentration of contaminating protein
Absorbance-based techniques such as a BCA assay or similar can
be used to quantify the total concentration of protein in a sample.
This is not specific to host cell proteins (HCPs) however. The
reason why this could be a problem is that BCA assay also
detects the proteins on the surface of viruses. Consequently, this
assay tends to overestimate the total mass of protein in a sample
especially if the concentration of HCPs is low and the
concentration of virus is high. Furthermore, these assays also
suffer from low sensitivity.
Specific HCP ELISA can be purchased for some cell lines,
although these assays only quantify specific proteins species in a
sample. If a specific HCP is defined as a CQA then this could
work well as long as the ELSIA used is able to identify the
concentration of the specific impurity in question. In the case of
the process developed in this thesis for vaccinia, there are no
commercial ELISA kits available for CV-1 cells, however there
are for Vero cells which also support vaccinia virus production.
o Contamination from media components such as serum, phenol red,
and antifoam.
If contaminants such as phenol red are defined as CQAs the
easiest way of ensuring safe concentrations of these substances in
the final material is to not use them. Commercial kits for the
analysis of residuals such as phenol red are not commercially
244
available hence these would need to be developed at a cost and
in-house using technologies such as mass spectrometry or reverse
phase HPLC.
o Concentration of common additives such as levels of residual
Benzonase, or detergents
The majority of process additives, Benzonase being a good
example, come with an associated specific assay. Residual
Benzonase can be quantified using an ELISA which is relatively
sensitive. These assays can be expensive, however the advantages
of adding materials such as Benzonase to ensure the removal of
DNA, can often outweigh the negative implications such as cost.
o Levels of contaminating endotoxin, mycoplasma
Endotoxins are quantified using ELISAs.
Mycoplasma can be semi-quantified using luminescence-based
assays. A more sensitive technique used to quantify mycoplasma
is qPCR. Mycoplasma can also be grown in culture and
quantified by counting associated colonies although this is time
consuming and variable.
o Removal of adventitious viruses
The removal of adventitious viruses is a challenge as common
techniques used in the biopharma sector such as nanofiltration
and low pH hold steps will remove and destroy vaccinia virus as
well as any adventitious agents.
Viral validation can still be performed on each unit operations in
the process to test for viral clearance, but processes without
245
specific capture steps will be optimised to recover viruses and so
the log removal of contaminating virus (LRV) will likely be low.
o Concentration of leachables from process consumables such as
chromatography columns and plastics.
Manufactures will test and validate consumables for leachables
prior to release. If leachables are biological in nature, analytical
assays, often in the form of ELISAs, will likely be supplied from
vendors. If the leachables are small molecules than gas
chromatography/mass spectrometry (GC/MS) is often used.
There is a validation challenge associated with ensuring that
leachables are within safe limits, however, this is not a problem
specific to viral therapies and hence, well developed best
practices and guidelines are available.
8.2 Process development
From the offset of process development, probably amongst the most important
challenges to be addressed for process validation of infectious virus processes generally
(not specific to vaccinia) is the scalability and productivity of the presently used
purification unit operations, arguably the most common being density gradient
ultracentrifugation. It has been suggested that chromatography using monoliths could
present an attractive alternative to ultracentrifugation namely owing to their high
binding capacity, high flow rate independent binding and resolution, and scalability
stated by the manufacturers of commercially available monoliths.
From a validation perspective it is likely that variation in the upstream process will
affect both the purity and yield of chromatographic unit operations (Wolff & Reichl
2011). Therefore it is difficult to determine robust operation parameters even when
246
using response service factorial designs such as DoE. Such tools are becoming more
and more common when approaching the submission of QbD filings, being stipulated in
the International Conference on Harmonisation of Technical Requirements for
Registration of Pharmaceuticals for Human Use (ICH) guidelines as good practice for
developing both process knowledge and process understanding.
One of the other major considerations when using chromatography is sanitization
validation, whether fixed or packed bed. Columns are not sold as sterile units, and the
vast majority of chromatography matrices cannot be autoclaved or steam sterilised. It is
for this reason that the regulators recommend a 0.2 µm sterile filter to be used up and
down stream of each chromatographic step for all buffers and product fractions. While
this is not a new hurdle in the world of chromatography, this will become more
challenging for large viruses that cannot be sterile filtered such as vaccinia. The reason
for this is that vaccinia virus is larger than 0.2 µm. To mitigate risk of batch failure,
appropriate control strategies will need to be covered in a risk assessment. This will
likely detail an enhanced in-process testing for endotoxin after each unit operation, as
well as reduced cycling of monoliths between batches in order to ensure the process
remains as closed as possible. Such risk assessment will increase costs upfront but will
reduce the risk of batch failures.
The reason why this has not been an issue up to now is that large viruses such as
vaccinia for use in the vaccine industry have been purified using ultracentrifugation,
which can be sterilised in place.
Facilities are currently being designed for processing of viral therapies that utilise single
use technologies with a 100% disposable flow paths in closed processes.
The primary recovery steps associated with viral bioprocesses can be a challenge to
close as cells often require lysis to release the virus. Chemical lysis does have some
247
operational advantages compared to high shear based unit operations such as
homogenisation from the point of view of single use, closed processing. The addition of
lysis buffers containing reagents such as Triton X-100 into viral harvest material can be
performed relatively easily in a closed system, while other cell disruption techniques
generally require stainless steel due to the high pressure drops required. Material contact
with stainless steel requires cleaning validation, and is more difficult to close. Other unit
operations such as sonication have also been used in the past, but have the same
limitations as homogenisation in terms single use functionality and ability to close.
Sonication is also difficult to scale up.
It is possible to close chromatographic unit operations, and especially with the
advancement of disposable flow path based systems such as the AKTA Ready (GE
Healthcare Uppsala, Sweden), as well as single use columns packed and shipped by
companies such as (RepliGen, Waltham MA, USA) single use chromatography is
becoming common place for clinical manufacturing.
Smaller viruses such as AAV can be sterile filtered using membranes typically used in
the mAb sector such as the Express SHC (0.5/0.2 µm filter) (Merck & Co. Kenilworth,
New Jersey, USA). AAV has an approximate diameter of 25 nm.
Larger viruses such as vaccinia (300 nm) cannot be sterile filtered; therefore it is likely
that columns will not be cycled across multiple batches, processes will need to be
closed, and release testing for endotoxin and bioburden as well as other adventitious
agents will be performed on selected in process samples as well as on each final bulk
product prior to vialing.
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8.3 Product release
Another challenge includes viral degradation and inactivation in standard processing
conditions (such as pH and salt) likely to be encountered during chromatography,
filtration or ultracentrifugation operations. The effect of long term storage on
biopharmaceuticals is an issue that has been discussed for many years. As well as
putting samples on long term stability studies, it has become increasingly common to
partake in accelerated stability studies in an attempt to identify drug candidates that are
particularly prone to degradation or oxidation causing the formation of product related
impurities such as aggregates and fragments leading to loss of potency. Arguably,
however, this is unlikely to effectively predict infectious viral stability as harsh
conditions often encountered during accelerated stability may give variable and
unrepresentative results, especially owing to the high levels of variability in infectivity
assays. While accelerated stability studies cannot be a substitution for long term
stability studies, it is useful when comparing a single product or process intermediate at
different conditions for likely effects of an environmental stimulant.
8.4 Consumables
As a column supplier, BIA Separations produces a number of chromatography columns
which need to be cGMP compliant. In order for this to be achieved, an array of batch
testing prior to release is required. This includes batch testing of column performance
by methods such as pulse response to measure HETP as well as integrity tests using
pressure drop and visual inspection as indicators of any damage. BIA Separations also
validates the nominal pore diameter of each batch of monolith prior to release to ensure
the specifications meet their in house quality standards. The appropriate paper work,
249
preferably electronic versions, is also expected to be produced on request. Batch records
from the production process will make up a large part of the data.
The aim of process validation is to demonstrate that the product is what the
manufacturer says it is, and, if undamaged during transport, the product behaves in the
way the process predicts when it arrives at the manufacturing site.
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Chapter 9
Conclusions and Future Work
9.1 Conclusions
Two capture steps have been developed and analysed using a range of orthogonal
analytical methods.
In order to achieve robust clearance of the required levels of dsDNA it is likely that a
second purification step will be required before direct injection of vaccinia virus into
patients. It is also likely that Benzonase endonuclease will need to be added to mitigate
the risk of higher than acceptable dsDNA levels being recovered in the final product
resulting in batch failure. As the likely specification of dsDNA is 10 ng/dose, the
requirement of a second step will ultimately depend on the dosing requirements, which
will depend on the therapeutic indication being targeted.
In summary, the developed process using CIM OH does present a reasonably robust and
saleable option for Vaccinia virus capture from crude cell lysate post clarification. CIM
OH was shown to be able to remove all quantifiable protein loaded to LLOQ levels. Up
to 99% of the total dsDNA loaded can be removed without nuclease treatment and
Vaccinia virus recovery, although variable due to cell based assay precision, was as
high as 90%.
Vaccinia virus holds great potential as an oncolytic and immunotherapeutic vaccine
against a broad spectrum of cancers. Purification of vaccinia virus, however, remains a
251
challenge. This is due to its large size, complex physicochemical properties, and highly
variable starting material that often contains cell debris, DNA, proteins and aggregate.
The overall aim of this EngD project was to evaluate, understand, and work to reduce
the current challenges faced by process scientists and engineers when developing
vaccinia virus for vaccines and oncolytic therapies.
The scalability and productivity of presently used purification unit operations were
identified early on as challenges.
The stability of vaccinia virus in commonly used processing buffers was evaluated,
subsequent to which, a monolith process was developed and analysed using a variety of
orthogonal analytical techniques. These included TCID50 to measure infectivity, NTA
and qPCR to measure the total number of particles and total number of viral genomes in
solution, TEM and SEM to analyse viral integrity and levels of aggregation, TRPS to
measure zeta potential, and BCA and PicoGreen to quantify total protein and DNA.
Vaccinia virus was shown to be able to adsorb to both CIEX and AIEX monoliths.
When loading CIM SO3 with crude vaccinia harvest, and after eluting in 50 mM
sodium phosphate 300 mM NaCl pH 7, DNA and total protein purification factors of up
to 4.3 and 4 respectively were demonstrated, as shown in Chapter 5. Large viral
aggregates were also shown to be removed after analysing load and elution fractions
using TEM. The trade-off when using CIM SO3 is that the recovery of infectious virus
drops from 84% to 35% when the NaCl concentration in the elution is reduced from 500
mM to 300 mM pH 7. The DNA purification factor is slightly lower when eluted at 500
mM NaCl, dropping from 4.3, to between 2.8 and 3.5. A reduction in step recovery
from 84% to 35% is shown in Section 7.5 to increase the CoG/dose by approximately
three fold.
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DNA and total protein purification factors achievable when using CIM OH monoliths
were higher than those achieved using CIM SO3. A 90% recovery of infectious virus is
achieved when vaccinia harvest is loading in 50 mM sodium phosphate 1 M ammonium
sulfate 2 M NaCl pH 8. All quantifiable protein is removed, and a DNA purification
factor of up to 88 is achievable. Levels of polydispersity are reduced, when analysed
using NTA, and large aggregates, imaged using SEM, are removed. The trade-off when
using CIM OH, is the recovery of infectious virus across the pre-filter. Ammonium
sulfate appears to cause vaccinia to aggregate significantly in the load, which results in
a recovery of approximately 35% across the filter. This reduces the overall recovery to
32%.
Throughout this work, multiple in-process samples were taken and kept at room
temperature for the duration of each experiment. All samples collected, were then
frozen together at the end of each chromatography run to insure that no systematic
errors would arise as a result of sample handling. It cannot be concluded with certainty
that changes in either sodium chloride concentration or pH have a significant effect on
vaccinia virus stability in sodium phosphate buffer due to the RSD in TCID50
measurements. Mean averaged values, shown in Figure 4.5, suggest that initial increases
in salt concentration reduced infectivity. The data also suggests that over time, at room
temperature, vaccinia virus appears to lose infectivity in DMEM, although once again,
robust conclusions are difficult to make with confidence due to the low precision of the
TCID50 assay.
Vaccinia titre, concentration of DNA and total protein, and levels of polydispersity
varied significantly from batch to batch and with increasing concentration of
ammonium sulfate. This had an impact on both the HIC and CIEX monolith
performance.
253
It is clear that the upstream, primary recovery and pre-filtration steps still need work. In
particular the primary recovery step used would be very difficult to scale up. Detergents
were not used in this study as there was concern that vaccinia would lose infectivity
post lysis therefore reducing viral yield. In the interest of time, a freeze thaw method,
which involved freezing and then thawing pelleted cells over 3 consecutive cycles, as
described in Section 3.2.2, was used. Some background on options available for cell
disruption is given in Chapter 2. Mechanical lysis may be an option for future work;
however, due to the risk category of the virus (Cat II) it was not possible to explore this
option.
Previously published work on vaccinia virus purification (Jordan et al. 2012;Wolff,
Siewert, Hansen, Faber, & Reichl 2010a;Wolff, Siewert, Lehmann, Hansen, Djurup,
Faber, & REICHL 2010b), suggests that CIM monoliths may perform slightly better
than membrane adsorbers, however, as previous studies were performed on a different
strain of vaccinia produced in a different cell line and measured predominantly with an
ELISA rather than TCID50, this conclusion should be taken with caution.
In comparison to ultracentrifugation, it is difficult to know how monoliths compare in
terms of impurity removal. Previous literature shows that large scale continuous
ultracentrifuges can recovery around 60% of infectious vaccinia virus from a crude
harvest. The industry is moving away from ultracentrifugation, although there remain
some advantages to the technology. Ultracentrifugation is a well-established technique
which makes process development relatively straightforward in comparison to
adsorption-based methods such as IEX or HIC. Using gradient media such as caesium
chloride, Iodixanol or sucrose, efficient separation of a number of viral vectors has been
documented (Dormond et al. 2010;Gias, Nielsen, Morgan, & Toms 2008;Otto-Wilhelm
254
Merten 2014). There are also significant disadvantages to using ultracentrifugation
technologies. Initial capital is high, and if volumetric throughput is low, or banding time
is high, then manufacturing costs, as shown in Section 7.4.2, increase exponentially.
CIM monoliths appear to offer some unique selectivity when purifying vaccinia virus.
Both CIM OH and CIM SO3 show potential as being valuable stationary phases in the
purification of oncolytic vaccinia viral constructs, and should be evaluated when
developing clinical and commercial processes. Variations in analytical measurements
make overall conclusions on process performance challenging, however, the use of
multiple orthogonal analytics allows for a qualitative and semi-quantitative picture of a
complex system to be built. This is critically important early on in process development
in order to build up levels of process knowledge and understanding.
In conclusion, the monolith-based purification process proposed in this thesis has the
enormous potential to produce oncolytic viral products in a more economically viable,
scalable and robust manner than previously possible. This will impact both academia
and industry by adding to the toolbox of unit operations that are able to purify vaccinia
virus. For academic groups it will lead to the production of more commercially
representative virus material for pre-clinical and proof of concepts studies. It will also
add value by enhancing our understanding of vaccinia stability, aggregation profiles and
binding kinetics. In an industrial setting it will facilitate the progression of a new and
potentially life changing class of viral products to treat cancer.
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In this work, the recorded dynamic binding capacity of infectious vaccinia virus was 1-
3x109 particles/mL of monolith.
However, Based on NTA data shown in Figure 5.6 the number of particles between 250
nm and 350 nm able to adsorb to CIM SO3 from the clarified viral harvest material post
0.8 µm filtration was 1.1x1011
/mL. The total number of particles between 0.5 and 1000
nm able to adsorb to the monolith was 2.3x1011
particles/mL.
It is unlikely that the theoretical capacity of 1.15x1013
is achievable.
A binding capacity of ~1x1011
capsids per mL may be a better estimate of maximum
achievable adsorption capacity; however both numbers should be used with caution.
It is known from this study that vaccinia virus aggregates and forms large complexes
especially in the presence of salt (Figure 4.12). The assumption, therefore, that particles
are able to adsorb in ordered monolayers is likely to be incorrect.
Monoliths have an open channel structure, however, as the channels are cross linking it
is possible that some of the monolith surface is unavailable for vaccinia adsorption, in
other words not in contact with the convective flow of the mobile phase. Mass transfer
into these regions would be limited by diffusion, which, due to the relatively large size
of vaccinia, would be very slow. It is also likely that charge repulsion and steric effects
would reduce the binding capacity of vaccinia particles onto the monolith surface.
The pressure drop measured across the load step was measure to 0.02 MPa on average.
Based on work by (Andrejcic and Podgornik 2017), and assuming a porosity of 60% for
1 mL 6 µm channel monoliths (Mao, Cernigoj, Zalokar, Strancar, & Kulozik 2017), the
deposited layer was calculated to be approximately 80 nm.
256
Based on the NTA data the majority of the adsorbed particles are not made up of
infectious virus, therefore a deposited layer of 80 nm seems reasonable based on the
average size of particles in the load.
9.2 Future work
9.2.1 Pre-filtration Yield Optimisation
Pre-filtration performance is one of the key areas of further work that is required to tech
transfer a cost effective vaccinia process into the clinic. In order to solve this problem,
there are a number of additional experiments that could be performed.
As shown in Figure 3.3, virus is diluted 1:5 in process buffer and then filtered through a
0.8µm cellulose acetate syringe filter prior to loading onto CIM monoliths. The
recovery of infectious virus across the filter without the presence of ammonium sulfate
in the diluent is approximately 60%. Switching the order of the dilution and filtration
steps around, as shown in Figure 9.1A, may solve the problem as ammonium sulfate
would be added to the feed stream after filtration. Whether this will work or not will
depend on the nature of the fouling material, and the impact of its removal on CIM OH
performance. It is possible that this approach could lead to high back pressures across
the monolith due to the presence of large viral aggregates in the feed steam; however, as
aggregates are still present in the harvest post filtration prior to column loading, fouling
as a result of viral aggregation seems unlikely.
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Harvest
Centrifugation
Dilution (1:5) in process buffer
Pre-Filtration (0.8 µm)
CIM OH
Formulation
2nd Chromatographic Step
Harvest
Centrifugation
Dilution (1:5) in process buffer
Pre-Filtration (0.8 µm)
CIM OH
Formulation
2nd Chromatographic Step
Harvest
Centrifugation
Dilution (1:5) in process buffer
Pre-Filtration (0.8 µm)
CIM SO3
Formulation
CIM OH
B
A
Figure 9.1 A) Proposed future study involving switching the order of the dilution and pre-
filtration steps B) Proposed order of monolith steps assuming CIM OH pre-filtration
continues to generate a low yield of infectious virus.
258
If back pressures are too high, or column performance is significantly reduced, an
alternative approach would be to trial a number of different pre-filters. Due to the size
of vaccinia virus (approximately 300 nm) a limited number of filters are available as
many have a nominal pore size below 0.3 µm. A non-exhaustive list of commercially
available filters is shown in Table 9.1.
Maximum throughput studies on normal flow membrane and depth filters would need to
be performed in triplicate. A typical pressure cut-off used is 20 psig. The filtrate would
need to be fractionated at regular intervals to analyse virus and impurity breakthrough.
Samples may also be measured for turbidity as this can signal breakthrough of
contaminants leading to fouling. Pmax studies can be used to predict the maximum
volume throughput achievable from large scale filters (van Reis and Zydney 2007) by
using models that assume either pore restriction or resistance build up from cake layer
formation. These can be useful when harvest volumes are limited, and time resources
are stretched, however, infectious virus recovery would be the main aim of these initial
studies.
Flux vs TMP studies can be performed on TFF membranes, to ensure TMP set points
operate within pressure dependant regions. The cross-flow can also be optimised to
ensure that sheer stress doesn’t cause viral capsid degradation resulting in membrane
fouling.
259
Table 9.1 Commercially available pre-column filters potentially applicable for vaccinia
virus prior to CIM
Types of
filter
Filter Name Chemistry Attributes Manufacturer
Depth filters
Polygard CR
Polypropylene 0.1 - 100 µm pore
size
Designed for low
fouling applications
EMD
Millipore
Millistak+Pod
Cellulose fibres and
Diatomaceous
Earth
Slight positive
charged
Designed for
primary recovery
and aggregate
removal
EMD
Millipore
Membrane filters (normal flow)
PolySep II Polypropylene Multi-layer
membrane
First layer 1-2 µm
Second layer 0.1-
1.2 µm
Designed for
fouling lipid feed
streams
EMD
Millipore
Polygard CN
Polypropylene 0.3 - 30 µm pore
size
Slightly
hydrophobic
EMD
Millipore
Membrane filter (cross flow)
KrosFlow hollow
fibre range
Modified
Polyethersulfone
(mPES),
Polysulfone (PS),
Mixed Ester (ME)
1KD to 0.65um
UF/DF/MF
Spectrum Labs
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If none of these strategies work, then CIM OH may need to be used as a second step.
The recovery of infectious virus from CIM SO3 is 60 – 70% when eluted in 500 mM
NaCl. Ammonium sulfate would need to be added to the CIM SO3 elution prior to
loading onto CIM OH, however, as the majority of the impurities would have been
removed by CIEX there may be no need for a pre-filtration step. This modified process
flow is shown in Figure 9.1B. In all experiments described, CIM OH pressure drop,
column lifetime and process performance will need to be monitored.
9.2.2 Development of an additional purification step to remove dsDNA
It would also be interesting to trial alternative chromatography columns such as SC-
MA, discussed in the literature (Wolff, Siewert, Hansen, Faber, & Reichl 2010a). CIM
monoliths are able to remove the majority of the DNA, however, depending on the dose
and the variability in DNA concentration in the harvest, an additional step may be
required. Depending on the percentage recovery achievable when SC-MA is challenged
with infectious vaccinia virus Lister strain; this may be a good option as a capture step.
CIM OH could then be trialled as a polishing step to remove DNA and protein, but
without the need for a pre-filtration step. When developed using MVA, SC-MA has a
DBC of approximately 6.42x109 pfu/capsule. If this is the same as for Lister strain, SC-
MA may provide better productivity than a monolith capture step as long as the
percentage recovery was comparable.
The challenge to developing a second step will be material availability. Vaccinia will
need to be produced, purified and then analysed before being available for polishing
261
step optimisation studies. Analytical throughput will also be a bottleneck as the number
of samples requiring analysis will double.
9.2.3 Qualification of assays to measure residual impurities
PicoGreen is routinely cited in the literature as a rapid, precise and accurate measure of
total dsDNA. However, some concerns have been recently raised as there is a possibility
that the PicoGreen reagent is able to pass through the viral envelope and protein capsid
and access the viral DNA. If this occurs then the assay may give misleading results as it
would overestimate the concentration of dsDNA in a sample. The magnitude of this
error would be dependent on viral concentration.
Due to resource limitations the assay was not fully qualified prior to use. A number of
attributes would therefore need to be assessed before PicoGreen could be used to
measure dsDNA contamination in either in-process samples or final drug substance
during a GMP manufacturing campaign.
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ICH guidelines suggest the following are assessed.
Precision
Accuracy
Sensitivity
Specificity
Range
Linearity
Robustness
PicoGreen reagent transport through the viral envelope would affect both specificity and
accuracy of the assay.
In order to assess specificity and accuracy, DNA spiking studies would need to be
performed. This would require the use of a fully characterised primary reference
standard. This standard would need to have a known concentration of intact viral
particles as well as an accurate concentration of dsDNA contamination, ideally
estimated using an orthogonal assay to PicoGreen such as qPCR.
Accuracy and specificity could then be determined by spiking lambda DNA into the
reference standard and measuring spike recovery using PicoGreen. This would assess
the ability of the assay to quantify the spike, as well as determine whether the virus was
interfering with the known concentration of dsDNA in the sample.
9.2.4 Further development of assays to quantify vaccinia virus
Analytical equipment known to be able to resolve vaccinia virus is limited.
The following list of analytical solutions were each evaluated during the project,
showed some promise but could not be extensively tested or included in the battery of
assays due to capital budget restrictions. Each has been covered in detail in Section 2.5.
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AIEX-HPLC, analytical monoliths (BIA Separations)
HIC-HPLC, analytical monoliths (BIA Separations)
TRPS, Tunable Resistive Pulse Sensing, measurements are based on
individual particle impedance as virus is passed through a stretchable
nanopore (IZON)
VirusCounter 3100 based on multi-channel flow cytometry detecting
presence of protein and DNA (ViroCyt, Sartorius)
Future work should include a thorough evaluation of these analytical techniques.
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Chapter 10
Appendix A
10.1 Residual Assay standard curves
As detailed in the materials and methods section of this thesis BCA and Quant-iT
PicoGreen assays were used to analyse total protein and dsDNA in vaccinia in process
samples.
The following graphs are copies of typical standard curves generated for each assay
BCA standard curves were generated using Bovine Serum Albumin (BSA)
Quant-iT PicoGreen standard curves were generated using λ DNA (isolated from is E.
coli bacteriophage)
Figure 10.1 Typical λ DNA standard curve.
y = 21.162x
R² = 0.9992
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
0 500 1000 1500 2000 2500
Flu
ore
scen
ce (
Ex =
480 n
m)
DNA Conc. (ng)
265
Figure 10.2 Typical BSA standard curve.
y = 0.0011x
R² = 0.9933
0.000
0.200
0.400
0.600
0.800
1.000
1.200
1.400
1.600
1.800
0 500 1000 1500 2000
Ab
sorb
an
ce (
562 n
m)
Protein Conc. (µg)
266
10.2 CoGs Model Parameters
WFI System
Effective Utilisation 60%
Percent Still Blowdown 10%
Storage Tank Fill Time Sizing (hr) 10
Bought-in WFI Yes
PW System
Effective Utilisation 60%
Storage Tank Fill Time Sizing (hr) 10
Cost of Works Breakdown
Installation 38%
Minor items 0%
Pipework 20%
HVAC 34%
Control 15%
Electrical Power 25%
Plant Utilities 3%
Building 65%
Fit out 14%
Exchange Rates
EU€ GB£ US$ CHF
EU€ 1.00 0.86 1.32 1.52
GB£ 1.16 1.00 1.50 1.76
US$ 0.76 0.67 1.00 1.08
CHF 0.66 0.57 0.93 1.00
Other Capital Breakdown
Validation 15%
Design Fee 16%
Construction Management 4%
Internal Capitalisation Costs
Validation engineering support 0%
Engineering indirects 0%
Other support 0%
User definable add-on 0%
267
Cleaning & Solution Prep
Flow rate (L/min/m) 50
L:D ratio for vessels 2
CIP Cycle Timings
PW rinse (min) 5
WFI rinse (min) 5
Caustic rinse (min) 5
Acid rinse(min) 5
Cleaning
CIP time (full) 1
CIP time (rinse) 0.2
CIP/SIP Personnel (#) 2
CIP Cycles per Day
Upstream 3
Media prep and hold 5
Downstream
5
Buffer prep and hold 10
Aqueous Waste Per m
3
Adj Cost
Non-Contaminated US$ 0.20
Contaminated US$ 0.86
Water Treatment Costs Per L
Adj Cost
PW Treatment US$ 0.05
Bought WFI Cost US$ 1.00
Contaminated Plastics Disposal Per T
Adj Cost
Incineration US$ 800.00
Landfill US$ 150.00
268
10.3 Publications by the Author
Vincent, D., Kramberger, P., Hudej, R., Štrancar, A., Wang, Y., Zhou, Y., &
Velayudhan, A. 2017. The development of a monolith-based purification process for
Orthopoxvirus vaccinia virus Lister strain. Journal of Chromatography A, 1524,
(Supplement C) 87-100 available from:
http://www.sciencedirect.com/science/article/pii/S0021967317313067
269
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